Unverified60% confidenceSolutionExact time
Video VQ-VAE, 3D convolution, and encoding uniformly sampled keyframes with ViT are the mainstream video tokenization methods.
2
Sources
60%
Confidence
Long-term
Relevance
8/7/2026
First Seen
Sources
为何只有Google做好了视频理解大模型？数据、架构与成本的三重壁垒
Related Claims
UnverifiedIn image-to-video generation, the conditioning image is typically encoded into a latent-space representation via a VAE and then concatenated with the noise sequence or injected into the generation process through cross-attention.78% similarUnverifiedVideo VAEs typically adopt 3D convolution or causal convolution structures to compress consecutive frames into a single temporal token, thereby reducing the computational burden of temporal modeling.77% similarUnverifiedKLING 3.0 features built-in audio generation capability, allowing it to generate sound effects that match the video content simultaneously with the video generation.73% similarUnverifiedVideoViT 是将 Vision Transformer 架构扩展至视频域的技术路线，通过在时间维度引入注意力机制建模帧间关系，并使用 Tubelet Embedding 将连续帧时空体素化为三维 patch72% similar
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