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COMPOSITION

DESIGN

COLOR

LIGHTING

  • Arto T. – A workflow for creating photorealistic, equirectangular 360° panoramas in ComfyUI using Flux

    pIXELsHAM.com

    https://civitai.com/models/735980/flux-equirectangular-360-panorama

    https://civitai.com/models/745010?modelVersionId=833115

    The trigger phrase is “equirectangular 360 degree panorama”. I would avoid saying “spherical projection” since that tends to result in non-equirectangular spherical images.

    Image resolution should always be a 2:1 aspect ratio. 1024 x 512 or 1408 x 704 work quite well and were used in the training data. 2048 x 1024 also works.

    I suggest using a weight of 0.5 – 1.5. If you are having issues with the image generating too flat instead of having the necessary spherical distortion, try increasing the weight above 1, though this could negatively impact small details of the image. For Flux guidance, I recommend a value of about 2.5 for realistic scenes.

    8-bit output at the moment

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    Read more: Arto T. – A workflow for creating photorealistic, equirectangular 360° panoramas in ComfyUI using Flux

  • Using HDR Light Studio via the Maya Connection

    pIXELsHAM.com

    http://www.cgmeetup.net/home/using-hdr-light-studio-via-the-maya-connection/

    Views : 1,384
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    Read more: Using HDR Light Studio via the Maya Connection

  • HDRI Median Cut plugin

    pIXELsHAM.com

    www.hdrlabs.com/picturenaut/plugins.html

     

     

    Note. The Median Cut algorithm is typically used for color quantization, which involves reducing the number of colors in an image while preserving its visual quality. It doesn’t directly provide a way to identify the brightest areas in an image. However, if you’re interested in identifying the brightest areas, you might want to look into other methods like thresholding, histogram analysis, or edge detection, through openCV for example.

     

    Here is an openCV example:

     

    # bottom left coordinates = 0,0
import numpy as np
import cv2

# Load the HDR or EXR image
image = cv2.imread('your_image_path.exr', cv2.IMREAD_UNCHANGED)  # Load as-is without modification

# Calculate the luminance from the HDR channels (assuming RGB format)
luminance = np.dot(image[..., :3], [0.299, 0.587, 0.114])

# Set a threshold value based on estimated EV
threshold_value = 2.4  # Estimated threshold value based on 4.8 EV

# Apply the threshold to identify bright areas
# The luminance array contains the calculated luminance values for each pixel in the image. # The threshold_value is a user-defined value that represents a cutoff point, separating "bright" and "dark" areas in terms of perceived luminance.
    thresholded = (luminance > threshold_value) * 255 

# Convert the thresholded image to uint8 for contour detection 
thresholded = thresholded.astype(np.uint8) 

# Find contours of the bright areas 
contours, _ = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 

# Create a list to store the bounding boxes of bright areas 
bright_areas = [] 

# Iterate through contours and extract bounding boxes for contour in contours: 
x, y, w, h = cv2.boundingRect(contour) 

# Adjust y-coordinate based on bottom-left origin 
y_bottom_left_origin = image.shape[0] - (y + h) bright_areas.append((x, y_bottom_left_origin, x + w, y_bottom_left_origin + h)) 

# Store as (x1, y1, x2, y2) 
# Print the identified bright areas 
print("Bright Areas (x1, y1, x2, y2):") for area in bright_areas: print(area)

     

    More details

     

    Luminance and Exposure in an EXR Image:

    • An EXR (Extended Dynamic Range) image format is often used to store high dynamic range (HDR) images that contain a wide range of luminance values, capturing both dark and bright areas.
    • Luminance refers to the perceived brightness of a pixel in an image. In an RGB image, luminance is often calculated using a weighted sum of the red, green, and blue channels, where different weights are assigned to each channel to account for human perception.
    • In an EXR image, the pixel values can represent radiometrically accurate scene values, including actual radiance or irradiance levels. These values are directly related to the amount of light emitted or reflected by objects in the scene.

     

    The luminance line is calculating the luminance of each pixel in the image using a weighted sum of the red, green, and blue channels. The three float values [0.299, 0.587, 0.114] are the weights used to perform this calculation.

     

    These weights are based on the concept of luminosity, which aims to approximate the perceived brightness of a color by taking into account the human eye’s sensitivity to different colors. The values are often derived from the NTSC (National Television System Committee) standard, which is used in various color image processing operations.

     

    Here’s the breakdown of the float values:

    • 0.299: Weight for the red channel.
    • 0.587: Weight for the green channel.
    • 0.114: Weight for the blue channel.

     

    The weighted sum of these channels helps create a grayscale image where the pixel values represent the perceived brightness. This technique is often used when converting a color image to grayscale or when calculating luminance for certain operations, as it takes into account the human eye’s sensitivity to different colors.

     

    For the threshold, remember that the exact relationship between EV values and pixel values can depend on the tone-mapping or normalization applied to the HDR image, as well as the dynamic range of the image itself.

     

    To establish a relationship between exposure and the threshold value, you can consider the relationship between linear and logarithmic scales:

    1. Linear and Logarithmic Scales:
      • Exposure values in an EXR image are often represented in logarithmic scales, such as EV (exposure value). Each increment in EV represents a doubling or halving of the amount of light captured.
      • Threshold values for luminance thresholding are usually linear, representing an actual luminance level.
    2. Conversion Between Scales:

      • To establish a mathematical relationship, you need to convert between the logarithmic exposure scale and the linear threshold scale.

      • One common method is to use a power function. For instance, you can use a power function to convert EV to a linear intensity value.



       

      threshold_value = base_value * (2 ** EV)



      Here, EV is the exposure value, base_value is a scaling factor that determines the relationship between EV and threshold_value, and 2 ** EV is used to convert the logarithmic EV to a linear intensity value.


    3. Choosing the Base Value:
      • The base_value factor should be determined based on the dynamic range of your EXR image and the specific luminance values you are dealing with.
      • You may need to experiment with different values of base_value to achieve the desired separation of bright areas from the rest of the image.

     

    Let’s say you have an EXR image with a dynamic range of 12 EV, which is a common range for many high dynamic range images. In this case, you want to set a threshold value that corresponds to a certain number of EV above the middle gray level (which is often considered to be around 0.18).

    Here’s an example of how you might determine a base_value to achieve this:

     

    # Define the dynamic range of the image in EV
dynamic_range = 12

# Choose the desired number of EV above middle gray for thresholding
desired_ev_above_middle_gray = 2

# Calculate the threshold value based on the desired EV above middle gray
threshold_value = 0.18 * (2 ** (desired_ev_above_middle_gray / dynamic_range))

print("Threshold Value:", threshold_value)
    Views : 843
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    Read more: HDRI Median Cut plugin

  • Neural Microfacet Fields for Inverse Rendering

    pIXELsHAM.com

    https://half-potato.gitlab.io/posts/nmf/

     

     

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    Read more: Neural Microfacet Fields for Inverse Rendering

  • What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…

    pIXELsHAM.com

    RASTERIZATION
    Rasterisation (or rasterization)     is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.

    For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution.

    In computer graphics, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium.

    With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing.

    Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices.

    Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel.

     

    The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization.

    There are also multiple problems:


    • If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice.



    • The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing.



    • You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view)



    • Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order)


     

     

     

    RAY CASTING
    It is almost the exact reverse of rasterization: you start from the virtual screen instead of the vector or 3D shapes, and you project a ray, starting from each pixel of the screen, until it intersect with a triangle.

    The cost is directly correlated to the number of pixels in the screen and you need a really cheap way of finding the first triangle that intersect a ray. In the end, it is more expensive than rasterization but it will, by design, ignore the triangles that are out of the field of view.

    You can use it to continue after the first triangle it hit, to take a little bit of the color of the next one, etc… This is useful to handle the border of the triangle cleanly (less jagged) and to handle transparency correctly.

     

    RAYTRACING


    Same idea as ray casting except once you hit a triangle you reflect on it and go into a different direction. The number of reflection you allow is the “depth” of your ray tracing. The color of the pixel can be calculated, based off the light source and all the polygons it had to reflect off of to get to that screen pixel.

    The easiest way to think of ray tracing is to look around you, right now. The objects you’re seeing are illuminated by beams of light. Now turn that around and follow the path of those beams backwards from your eye to the objects that light interacts with. That’s ray tracing.

    Ray tracing is eye-oriented process that needs walking through each pixel looking for what object should be shown there, which is also can be described as a technique that follows a beam of light (in pixels) from a set point and simulates how it reacts when it encounters objects.

    Compared with rasterization, ray tracing is hard to be implemented in real time, since even one ray can be traced and processed without much trouble, but after one ray bounces off an object, it can turn into 10 rays, and those 10 can turn into 100, 1000…The increase is exponential, and the the calculation for all these rays will be time consuming.

    Historically, computer hardware hasn’t been fast enough to use these techniques in real time, such as in video games. Moviemakers can take as long as they like to render a single frame, so they do it offline in render farms. Video games have only a fraction of a second. As a result, most real-time graphics rely on the another technique called rasterization.

     

     

    PATH TRACING
    Path tracing can be used to solve more complex lighting situations.
    Path tracing is a type of ray tracing. When using path tracing for rendering, the rays only produce a single ray per bounce. The rays do not follow a defined line per bounce (to a light, for example), but rather shoot off in a random direction. The path tracing algorithm then takes a random sampling of all of the rays to create the final image. This results in sampling a variety of different types of lighting.

    When a ray hits a surface it doesn’t trace a path to every light source, instead it bounces the ray off the surface and keeps bouncing it until it hits a light source or exhausts some bounce limit.
    It then calculates the amount of light transferred all the way to the pixel, including any color information gathered from surfaces along the way.
    It then averages out the values calculated from all the paths that were traced into the scene to get the final pixel color value.

    It requires a ton of computing power and if you don’t send out enough rays per pixel or don’t trace the paths far enough into the scene then you end up with a very spotty image as many pixels fail to find any light sources from their rays. So when you increase the the samples per pixel, you can see the image quality becomes better and better.

    Ray tracing tends to be more efficient than path tracing. Basically, the render time of a ray tracer depends on the number of polygons in the scene. The more polygons you have, the longer it will take.
    Meanwhile, the rendering time of a path tracer can be indifferent to the number of polygons, but it is related to light situation: If you add a light, transparency, translucence, or other shader effects, the path tracer will slow down considerably.

     
     

     

    Sources:
    https://medium.com/@junyingw/future-of-gaming-rasterization-vs-ray-tracing-vs-path-tracing-32b334510f1f

     

    ELI5: What's the difference among Rasterization, Ray Casting and Ray Tracing ?
    byu/pixie_laluna inexplainlikeimfive

     

    blogs.nvidia.com/blog/2018/03/19/whats-difference-between-ray-tracing-rasterization/

     

    https://en.wikipedia.org/wiki/Rasterisation

     

    Path Tracing vs Ray Tracing

     

    https://www.quora.com/Whats-the-difference-between-ray-tracing-and-path-tracing

    Views : 15,882
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    Read more: What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…

  • DUNE | DP Greig Fraser ACS, ASC | ShotDeck: Shot Talk

    pIXELsHAM.com
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    Read more: DUNE | DP Greig Fraser ACS, ASC | ShotDeck: Shot Talk

  • Pixel Cinematography – A Lighting Approach for Computer Graphics – Siggraph 1996

    pIXELsHAM.com

    http://media.siggraph.org/education/cgsource/Archive/ConfereceCourses/S96/course30.pdf

    Views : 1,765
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  • Examples Of Creative Lighting Techniques In Food and product Photography

    pIXELsHAM.com

    http://digitalsynopsis.com/design/photography-creative-lighting-techniques/

    Views : 2,108
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    Read more: Examples Of Creative Lighting Techniques In Food and product Photography

  • Composition – cinematography Cheat Sheet

    pIXELsHAM.com

    https://moodle.gllm.ac.uk/pluginfile.php/190622/mod_resource/content/1/Cinematography%20Cheat%20Sheet.pdf

    Where is our eye attracted first? Why?

    Size. Focus. Lighting. Color.

    Size. Mr. White (Harvey Keitel) on the right.
    Focus. He’s one of the two objects in focus.
    Lighting. Mr. White is large and in focus and Mr. Pink (Steve Buscemi) is highlighted by
    a shaft of light.
    Color. Both are black and white but the read on Mr. White’s shirt now really stands out.


    What type of lighting?

    -> High key lighting.
    Features bright, even illumination and few conspicuous shadows. This lighting key is often used in musicals and comedies.

    Low key lighting
    Features diffused shadows and atmospheric pools of light. This lighting key is often used in mysteries and thrillers.

    High contrast lighting
    Features harsh shafts of lights and dramatic streaks of blackness. This type of lighting is often used in tragedies and melodramas.

     

    What type of shot?

    Extreme long shot
    Taken from a great distance, showing much of the locale. Ifpeople are included in these shots, they usually appear as mere specks

    -> Long shot
    Corresponds to the space between the audience and the stage in a live theater. The long shots show the characters and some of the locale.

    Full shot
    Range with just enough space to contain the human body in full. The full shot shows the character and a minimal amount of the locale.

    Medium shot
    Shows the human figure from the knees or waist up.

    Close-Up
    Concentrates on a relatively small object and show very little if any locale.

    Extreme close-up
    Focuses on an unnaturally small portion of an object, giving that part great detail and symbolic significance.

     

    What angle?

    Bird’s-eye view.
    The shot is photographed directly from above. This type of shot can be disorienting, and the people photographed seem insignificant.

    High angle.
    This angle reduces the size of the objects photographed. A person photographed from this angle seems harmless and insignificant, but to a lesser extent than with the bird’s-eye view.

    -> Eye-level shot.
    The clearest view of an object, but seldom intrinsically dramatic, because it tends to be the norm.

    Low angle.
    This angle increases high and a sense of verticality, heightening the importance of the object photographed. A person shot from this angle is given a sense of power and respect.

    Oblique angle.
    For this angle, the camera is tilted laterally, giving the image a slanted appearance. Oblique angles suggest tension, transition, a impending movement. They are also called canted or dutch angles.

     

    What is the dominant color?

    The use of color in this shot is symbolic. The scene is set in warehouse. Both the set and characters are blues, blacks and whites.

    This was intentional allowing for the scenes and shots with blood to have a great level of contrast.

     

    What is the Lens/Filter/Stock?

    Telephoto lens.
    A lens that draws objects closer but also diminishes the illusion of depth.

    Wide-angle lens.
    A lens that takes in a broad area and increases the illusion of depth but sometimes distorts the edges of the image.

    Fast film stock.
    Highly sensitive to light, it can register an image with little illumination. However, the final product tends to be grainy.

    Slow film stock.
    Relatively insensitive to light, it requires a great deal of illumination. The final product tends to look polished.

    The lens is not wide-angle because there isn’t a great sense of depth, nor are several planes in focus. The lens is probably long but not necessarily a telephoto lens because the depth isn’t inordinately compressed.

    The stock is fast because of the grainy quality of the image.

     

    Subsidiary Contrast; where does the eye go next?

    The two guns.

     

    How much visual information is packed into the image? Is the texture stark, moderate, or highly detailed?

    Minimalist clutter in the warehouse allows a focus on a character driven thriller.

     

    What is the Composition?

    Horizontal.
    Compositions based on horizontal lines seem visually at rest and suggest placidity or peacefulness.

    Vertical.
    Compositions based on vertical lines seem visually at rest and suggest strength.

    -> Diagonal.
    Compositions based on diagonal, or oblique, lines seem dynamic and suggest tension or anxiety.

    -> Binary. Binary structures emphasize parallelism.

    Triangle.
    Triadic compositions stress the dynamic interplay among three main

    Circle.
    Circular compositions suggest security and enclosure.

     

    Is the form open or closed? Does the image suggest a window that arbitrarily isolates a fragment of the scene? Or a proscenium arch, in which the visual elements are carefully arranged and held in balance?

    The most nebulous of all the categories of mise en scene, the type of form is determined by how consciously structured the mise en scene is. Open forms stress apparently simple techniques, because with these unself-conscious methods the filmmaker is able to emphasize the immediate, the familiar, the intimate aspects of reality. In open-form images, the frame tends to be deemphasized. In closed form images, all the necessary information is carefully structured within the confines of the frame. Space seems enclosed and self-contained rather than continuous.

    Could argue this is a proscenium arch because this is such a classic shot with parallels and juxtapositions.

     

    Is the framing tight or loose? Do the character have no room to move around, or can they move freely without impediments?

    Shots where the characters are placed at the edges of the frame and have little room to move around within the frame are considered tight.

    Longer shots, in which characters have room to move around within the frame, are considered loose and tend to suggest freedom.

    Center-framed giving us the entire scene showing isolation, place and struggle.

     

    Depth of Field. On how many planes is the image composed (how many are in focus)? Does the background or foreground comment in any way on the mid-ground?

    Standard DOF, one background and clearly defined foreground.

     

    Which way do the characters look vis-a-vis the camera?

    An actor can be photographed in any of five basic positions, each conveying different psychological overtones.

    Full-front (facing the camera):
    the position with the most intimacy. The character is looking in our direction, inviting our complicity.

    Quarter Turn:
    the favored position of most filmmakers. This position offers a high degree of intimacy but with less emotional involvement than the full-front.

    -> Profile (looking of the frame left or right):
    More remote than the quarter turn, the character in profile seems unaware of being observed, lost in his or her own thoughts.

    Three-quarter Turn:
    More anonymous than the profile, this position is useful for conveying a character’s unfriendly or antisocial feelings, for in effect, the character is partially turning his or her back on us, rejecting our interest.

    Back to Camera:
    The most anonymous of all positions, this position is often used to suggest a character’s alienation from the world. When a character has his or her back to the camera, we can only guess what’s taking place internally, conveying a sense of concealment, or mystery.

    How much space is there between the characters?

    Extremely close, for a gunfight.

     

    The way people use space can be divided into four proxemic patterns.

    Intimate distances.
    The intimate distance ranges from skin contact to about eighteen inches away. This is the distance of physical involvement–of love, comfort, and tenderness between individuals.

    -> Personal distances.
    The personal distance ranges roughly from eighteen inches away to about four feet away. These distances tend to be reserved for friends and acquaintances. Personal distances preserve the privacy between individuals, yet these rages don’t necessarily suggest exclusion, as intimate distances often do.

    Social distances.
    The social distance rages from four feet to about twelve feet. These distances are usually reserved for impersonal business and casual social gatherings. It’s a friendly range in most cases, yet somewhat more formal than the personal distance.

    Public distances.
    The public distance extends from twelve feet to twenty-five feet or more. This range tends to be formal and rather detached.

    Views : 2,482
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  • Car and automotive lighting

    pIXELsHAM.com

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  • Outpost VFX lighting tips

    pIXELsHAM.com

    www.outpost-vfx.com/en/news/18-pro-tips-and-tricks-for-lighting

     

    Get as much information regarding your plate lighting as possible

    • Always use a reference
    • Replicate what is happening in real life
    • Invest into a solid HDRI
    • Start Simple
    • Observe real world lighting, photography and cinematography
    • Don’t neglect the theory
    • Learn the difference between realism and photo-realism.
    • Keep your scenes organised

     

    Views : 639
    Read more: Outpost VFX lighting tips

  • Tracing Spherical harmonics and how Weta used them in production

    pIXELsHAM.com

     

    A way to approximate complex lighting in ultra realistic renders.

    All SH lighting techniques involve replacing parts of standard lighting equations with spherical functions that have been projected into frequency space using the spherical harmonics as a basis.

    http://www.cs.columbia.edu/~cs4162/slides/spherical-harmonic-lighting.pdf

     

    Spherical harmonics as used at Weta Digital

    https://www.fxguide.com/fxfeatured/the-science-of-spherical-harmonics-at-weta-digital/

    Views : 1,188
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  • Victor Perez – CA Color Management Fundamentals & ACES Workflows in Foundry Nuke

    pIXELsHAM.com
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    Read more: Victor Perez – CA Color Management Fundamentals & ACES Workflows in Foundry Nuke

  • domeble – Hi-Resolution CGI Backplates and 360° HDRI

    pIXELsHAM.com

    www.domeble.com/

    When collecting hdri make sure the data supports basic metadata, such as:

    • Iso
    • Aperture
    • Exposure time or shutter time
    • Color temperature
    • Color space Exposure value (what the sensor receives of the sun intensity in lux)
    • 7+ brackets (with 5 or 6 being the perceived balanced exposure)

     

    In image processing, computer graphics, and photography, high dynamic range imaging (HDRI or just HDR) is a set of techniques that allow a greater dynamic range of luminances (a Photometry measure of the luminous intensity per unit area of light travelling in a given direction. It describes the amount of light that passes through or is emitted from a particular area, and falls within a given solid angle) between the lightest and darkest areas of an image than standard digital imaging techniques or photographic methods. This wider dynamic range allows HDR images to represent more accurately the wide range of intensity levels found in real scenes ranging from direct sunlight to faint starlight and to the deepest shadows.

     

    The two main sources of HDR imagery are computer renderings and merging of multiple photographs, which in turn are known as low dynamic range (LDR) or standard dynamic range (SDR) images. Tone Mapping (Look-up) techniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect. Photography

     

    In photography, dynamic range is measured in Exposure Values (in photography, exposure value denotes all combinations of camera shutter speed and relative aperture that give the same exposure. The concept was developed in Germany in the 1950s) differences or stops, between the brightest and darkest parts of the image that show detail. An increase of one EV or one stop is a doubling of the amount of light.

     

    The human response to brightness is well approximated by a Steven’s power law, which over a reasonable range is close to logarithmic, as described by the Weber�Fechner law, which is one reason that logarithmic measures of light intensity are often used as well.

     

    HDR is short for High Dynamic Range. It’s a term used to describe an image which contains a greater exposure range than the “black” to “white” that 8 or 16-bit integer formats (JPEG, TIFF, PNG) can describe. Whereas these Low Dynamic Range images (LDR) can hold perhaps 8 to 10 f-stops of image information, HDR images can describe beyond 30 stops and stored in 32 bit images.

     

    Views : 1,863
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  • Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesis

    pIXELsHAM.com

    https://srameo.github.io/projects/le3d/

     

    LE3D is a method for real-time HDR view synthesis from RAW images. It is particularly effective for nighttime scenes.

     

    https://github.com/Srameo/LE3D

     

    Views : 78
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    Read more: Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesis

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