168 citations · 239 across the 15 of their papers we have counts for
20 papers
Learning Realistic Athletic Sprinting Without Demonstrations
William Wang, Nicholas Bianco, Guy Tevet +4
We present a muscle-driven simulation system for generating biomechanically accurate motion for high-speed athletic locomotion tasks that does not require motion demonstrations. Ou…
Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA
Michael Piseno, Guy Tevet, C. Karen Liu
We present Cloak, a training recipe that endows a Vision-Language-Action (VLA) model with zero-shot cross-embodiment transfer by cloaking the end-effector from its own wrist camera…
RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation
Jiahao Zhang, Joseph Liu, Young-Yoon Lee +9
Success in generative modeling across language, image, and video demonstrates that large, well-curated datasets are the key driver for building capable models. 3D Human motion, how…
SMP: Reusable Score-Matching Motion Priors for Physics-Based Character Control
Yuxuan Mu, Ziyu Zhang, Yi Shi +9
Data-driven motion priors that can guide agents toward producing naturalistic behaviors play a pivotal role in creating life-like virtual characters. Adversarial imitation learning…
Generating Detailed Character Motion from Blocking Poses
Purvi Goel, Guy Tevet, C. K. Liu +1
We focus on the problem of using generative diffusion models for the task of motion detailing: converting a rough version of a character animation, represented by a sparse set of c…
Express4D: Expressive, Friendly, and Extensible 4D Facial Motion Generation Benchmark
Yaron Aloni, Rotem Shalev-Arkushin, Yonatan Shafir +3
Dynamic facial expression generation from natural language is a crucial task in Computer Graphics, with applications in Animation, Virtual Avatars, and Human-Computer Interaction.…
BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion
Qiayuan Liao, Takara E. Truong, Xiaoyu Huang +4
The human-like form of humanoid robots positions them uniquely to achieve the agility and versatility in motor skills that humans possess. Learning from human demonstrations offers…
HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
Roey Ron, Guy Tevet, Haim Sawdayee +1
We present HOIDiNi, a text-driven diffusion framework for synthesizing realistic and plausible human-object interaction (HOI). HOI generation is extremely challenging since it indu…
Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion
Haim Sawdayee, Chuan Guo, Guy Tevet +3
Text-to-motion generative models span a wide range of 3D human actions but struggle with nuanced stylistic attributes such as a "Chicken" style. Due to the scarcity of style-specif…
AnyTop: Character Animation Diffusion with Any Topology
Inbar Gat, Sigal Raab, Guy Tevet +3
Generating motion for arbitrary skeletons is a longstanding challenge in computer graphics, remaining largely unexplored due to the scarcity of diverse datasets and the irregular n…
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control
Guy Tevet, Sigal Raab, Setareh Cohan +5
Motion diffusion models and Reinforcement Learning (RL) based control for physics-based simulations have complementary strengths for human motion generation. The former is capable…
Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer
Sigal Raab, Inbar Gat, Nathan Sala +5
Given the remarkable results of motion synthesis with diffusion models, a natural question arises: how can we effectively leverage these models for motion editing? Existing diffusi…
Flexible Motion In-betweening with Diffusion Models
Setareh Cohan, Guy Tevet, Daniele Reda +2
Motion in-betweening, a fundamental task in character animation, consists of generating motion sequences that plausibly interpolate user-provided keyframe constraints. It has long…
MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion
Roy Kapon, Guy Tevet, Daniel Cohen-Or +1
We introduce Multi-view Ancestral Sampling (MAS), a method for 3D motion generation, using 2D diffusion models that were trained on motions obtained from in-the-wild videos. As suc…
Human Motion Diffusion as a Generative Prior
Yonatan Shafir, Guy Tevet, Roy Kapon +1
Recent work has demonstrated the significant potential of denoising diffusion models for generating human motion, including text-to-motion capabilities. However, these methods are…
Single Motion Diffusion
Sigal Raab, Inbal Leibovitch, Guy Tevet +3
Synthesizing realistic animations of humans, animals, and even imaginary creatures, has long been a goal for artists and computer graphics professionals. Compared to the imaging do…
Human Motion Diffusion Model
Guy Tevet, Sigal Raab, Brian Gordon +3
Natural and expressive human motion generation is the holy grail of computer animation. It is a challenging task, due to the diversity of possible motion, human perceptual sensitiv…
MotionCLIP: Exposing Human Motion Generation to CLIP Space
Guy Tevet, Brian Gordon, Amir Hertz +2
We introduce MotionCLIP, a 3D human motion auto-encoder featuring a latent embedding that is disentangled, well behaved, and supports highly semantic textual descriptions. MotionCL…
Evaluating the Evaluation of Diversity in Natural Language Generation
Guy Tevet, Jonathan Berant
Despite growing interest in natural language generation (NLG) models that produce diverse outputs, there is currently no principled method for evaluating the diversity of an NLG sy…
Evaluating Text GANs as Language Models
Guy Tevet, Gavriel Habib, Vered Shwartz +1
Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of ``exposure bi…