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Found 11 papers, 4 papers with code
PACE: Human and Camera Motion Estimation from in-the-wild Videos
This design combines the strengths of SLAM and motion priors, which leads to significant improvements in human and camera motion estimation.
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual characters.
Synthesizing Physical Character-Scene Interactions
These scene interactions are learned using an adversarial discriminator that evaluates the realism of a motion within the context of a scene.
PADL: Language-Directed Physics-Based Character Control
In this work, we present PADL, which leverages recent innovations in NLP in order to take steps towards developing language-directed controllers for physics-based character animation.
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim.
Factory: Fast Contact for Robotic Assembly
We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics.
ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
By leveraging a massively parallel GPU-based simulator, we are able to train skill embeddings using over a decade of simulated experiences, enabling our model to learn a rich and versatile repertoire of skills.
Physics-based Human Motion Estimation and Synthesis from Videos
By enabling learning of motion synthesis from video, our method paves the way for large-scale, realistic and diverse motion synthesis.
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU.
KAMA: 3D Keypoint Aware Body Mesh Articulation
We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints.
Ranked #48 on
3D Human Pose Estimation
on 3DPW
UniCon: Universal Neural Controller For Physics-based Character Motion
The field of physics-based animation is gaining importance due to the increasing demand for realism in video games and films, and has recently seen wide adoption of data-driven techniques, such as deep reinforcement learning (RL), which learn control from (human) demonstrations.
