Summary of Multi-track Timeline Control For Text-driven 3d Human Motion Generation, by Mathis Petrovich et al.
Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation
by Mathis Petrovich, Or Litany, Umar Iqbal, Michael J. Black, Gül Varol, Xue Bin Peng, Davis Rempe
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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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 As machine learning educators, we can summarize the abstract of this paper by saying that recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text. However, using a single text prompt as input lacks fine-grained control needed by animators. To address this, the authors introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive yet fine-grained input interface for users. The proposed method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. Experimental comparisons and ablations validate that the method produces realistic motions that respect the semantics and timing of given text prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating 3D animations from text descriptions. Right now, you can only give a single prompt and get an animation based on that. But animators need more control than that! They want to be able to specify exact timings for different actions and compose multiple actions together. To make this happen, the authors created a new way of giving input called timeline control. This lets users specify multiple prompts, each with its own timing, and then the computer generates an animation based on those prompts. |
Keywords
* Artificial intelligence * Diffusion model * Machine learning * Prompt * Semantics
