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Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Denoising diffusion models have shown great promise in human motion synthesis conditioned on natural language descriptions. However, integrating spatial constraints, such as pre-defined motion trajectories and obstacles, remains a challenge despite being essential for bridging the gap between isolated human motion and its surrounding environment. To address this issue, we propose Guided Motion Diffusion (GMD), a method that incorporates spatial constraints into the motion generation process. Specifically, we propose an effective feature projection scheme that manipulates motion representation to enhance the coherency between spatial information and local poses. Together with a new imputation formulation, the generated motion can reliably conform to spatial constraints such as global motion trajectories. Furthermore, given sparse spatial constraints (e.g. sparse keyframes), we introduce a new dense guidance approach to turn a sparse signal, which is susceptible to being ignored during the reverse steps, into denser signals to guide the generated motion to the given constraints. Our extensive experiments justify the development of GMD, which achieves a significant improvement over state-of-the-art methods in text-based motion generation while allowing control of the synthesized motions with spatial constraints. Show more
Publication status
publishedExternal links
Book title
2023 IEEE/CVF International Conference on Computer Vision (ICCV)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
09686 - Tang, Siyu / Tang, Siyu
Funding
204840 - Learning to Create Realistic Human Avatars (SNF)
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ETH Bibliography
yes
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