ELMA: Benchmarking Anaphoric Compositions for Long-Term Text-to-Motion Generation
To mitigate this, we present ELMA, an episodic long-term motion dataset with dense segment-level annotations that preserve cross-segment dependencies.
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Key points
- Text-guided long-term human motion generation is essential for applications such as continuously operating digital avatars.
- Despite recent progress on short clips, existing models struggle with multi-segment instructions that involve anaphora, where constraints established in earlier segments must be preserved over time.
- In this paper, we introduce a novel task of long-term motion generation from such anaphoric compositions.
- We demonstrate that ELMA enables significant improvements in both global physical realism and semantic alignment, setting a robust new baseline for long-term motion synthesis.
Sources (1)
- [1]ELMA: Benchmarking Anaphoric Compositions for Long-Term Text-to-Motion GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 01:05 PM
To mitigate this, we present ELMA, an episodic long-term motion dataset with dense segment-level annotations that preserve cross-segment dependencies.
Text-guided long-term human motion generation is essential for applications such as continuously operating digital avatars.
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