Research map / Video / Action recognition
Generating Videos with Scene Dynamics
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train a video GAN rather than an autoencoder with a GMM latent sampler | VGAN · Video generation, Golf, Beach, Train, and Baby | human preference (%): 88, 83, 87, 71 (Mean 82) | Helped |
| add separate foreground and stationary-background streams (ablation) | VGAN · Video generation, Golf, Beach, Train, and Baby | human preference over VGAN One Stream (%): 55, 58, 47, 52 (Mean 53) | Mixed |
| use one stream instead of separate foreground and background streams (ablation) | VGAN · Video generation, Golf, Beach, Train, and Baby | human preference over Autoencoder (%): 85, 88, 85, 73 (Mean 82) | Helped |
| generate video instead of showing a static frame | VGAN · Video generation | preference for static frame (%): 38% | Helped |
Closest papers by meaning
- Towards Smooth Video Composition (ICLR 2023 · similarity 0.90)
- Hierarchical Video Generation From Orthogonal Information: Optical Flow and Texture (AAAI 2018 · similarity 0.89)
- Generating Long Videos of Dynamic Scenes (NeurIPS 2022 · similarity 0.89)
- DynaTokens: Teaching Dynamics to Camera-Controlled Video Models at Test Time (NeurIPS 2026 · similarity 0.87)
- Generative Image Dynamics (CVPR 2024 · similarity 0.87)
- Motion Prompting: Controlling Video Generation with Motion Trajectories (CVPR 2025 · similarity 0.87)
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