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We present TeSMo ( ), a text-controlled scene-aware motion generation method based on denoising diffusion models. It’s an exciting collaboration with Justus Thies, Michael Black, Jason Peng, Davis Rempe. (1/7)

45,744 görüntüleme • 2 yıl önce •via X (Twitter)

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Given a 3D scene and a target interaction object, our goal is to generate a plausible human-scene interaction, where a user-specified text prompt can control the motion style. (2/7)

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Since there are few datasets with motion, text, and 3D scenes together, we propose to fine-tune a powerful pre-trained text-to-motion motion with a new scene-aware branch. (3/7)

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First, we pre-train a scene-agnostic text-to-motion diffusion model on a large motion-capture dataset, prioritizing learning to reach goal locations. Then, we augment it with a scene-aware component, finetuning with augmented data containing detailed scene information. (4/7)

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We create the Loco-3D-Front dataset to learn navigation, by integrating locomotion sequences from HumanML3D into diverse 3D environments from 3D-FRONT. For interactions, we annotate text descriptions for sub-sequences from the SAMP dataset. (5/7)

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Our method successfully generates realistic motions that navigate around obstacles while being conditioned on various text prompts. (6/7)

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For interactions, diverse text descriptions help disambiguate between actions like sitting or standing up, and even allow for stylizing sitting motions, such as crossing arms. (7/7)

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@JustusThies @Michael_J_Black @xbpeng4 @davrempe nice work, congrats!

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@JustusThies @Michael_J_Black @xbpeng4 @davrempe Looks amazing - congrats!

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Frank (Haofan) Wang2 yıl önce

@JustusThies @Michael_J_Black @xbpeng4 @davrempe 恭喜易老哥毕业🎓

Benzer Videolar

Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

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