In the retina, photoreceptors, bipolar cells, and horizontal cells work together to process visual information before it reaches the brain. Here’s how each cell type contributes to vision:
Sources familiar with details of the production pegged the cost of the first nine 40-minute episodes at north of $80 million; the second batch of nine about to air has a price tag approaching $100 million. What drove the cost far beyond typical animation expenses, insiders say, were both a labor-intensive approach and frequent cost overruns triggered by delayed script deliveries after the second season was put into production with only a fraction of the season written.
But even more eyebrow-raising than the production cost was that Riot spent $60 million of its own money to promote the first season of “Arcane,” exponentially more than a studio would typically spend for a show it isn’t distributing — and far more than Netflix itself spent ($4 million per episode). Reps for the streaming service declined to comment for this article.
Bella works in spectral space, allowing effects such as BSDF wavelength dependency, diffraction, or atmosphere to be modeled far more accurately than in color space.
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Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time tuning of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.
