Summary of Dimensionx: Create Any 3d and 4d Scenes From a Single Image with Controllable Video Diffusion, by Wenqiang Sun et al.
DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion
by Wenqiang Sun, Shuo Chen, Fangfu Liu, Zilong Chen, Yueqi Duan, Jun Zhang, Yikai Wang
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DimensionX, a framework that generates photorealistic 3D and 4D scenes from a single image using video diffusion. The approach leverages the idea that spatial structure and temporal evolution can be represented through sequences of video frames. While recent video diffusion models excel in producing vivid visuals, they lack control over spatial and temporal factors during generation. DimensionX addresses this by proposing ST-Director, which decouples spatial and temporal factors using dimension-aware LoRAs learned from dimension-variant data. This allows for precise manipulation of spatial structure and temporal dynamics, enabling the reconstruction of 3D and 4D representations. The authors also introduce a trajectory-aware mechanism for 3D generation and an identity-preserving denoising strategy for 4D generation. Extensive experiments on various datasets demonstrate that DimensionX outperforms previous methods in controllable video generation and 3D/4D scene generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a machine that can make realistic 3D and 4D videos from just one picture. It uses a special way of looking at video frames to understand how objects move and change over time. The new method, called DimensionX, lets the computer control how things look in different directions and how they move through time. This is helpful because it can create more realistic videos that show real-world scenes. The authors tested their method on many different pictures and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Diffusion
