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Found 13 papers, 5 papers with code
GAN-based Garment Generation Using Sewing Pattern Images
The generation of realistic apparel model has become increasingly popular as a result of the rapid pace of change in fashion trends and the growing need for garment models in various applications such as virtual try-on.
Dreamitate: Real-World Visuomotor Policy Learning via Video Generation
A key challenge in manipulation is learning a policy that can robustly generalize to diverse visual environments.
SHARE: Single-view Human Adversarial REconstruction
We perform a comprehensive analysis on the impact of camera poses on HPS reconstruction outcomes.
ViLA: Efficient Video-Language Alignment for Video Question Answering
In this work, we propose an efficient Video-Language Alignment (ViLA) network.
Ranked #1 on
Video Question Answering
on NExT-QA (Efficient)
MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation
Furthermore, via experiments on the NYUv2 and IBims-1 datasets, we demonstrate that these enhanced representations translate to performance improvements in both the in-distribution and out-of-distribution settings.
Ranked #19 on
Monocular Depth Estimation
on NYU-Depth V2
ICAR: Image-based Complementary Auto Reasoning
Based on this notion, we propose a compatibility learning framework, a category-aware Flexible Bidirectional Transformer (FBT), for visual "scene-based set compatibility reasoning" with the cross-domain visual similarity input and auto-regressive complementary item generation.
Differentiable Simulation of Soft Multi-body Systems
We present a method for differentiable simulation of soft articulated bodies.
Efficient Differentiable Simulation of Articulated Bodies
We derive the gradients of the forward dynamics using spatial algebra and the adjoint method.
Scalable Differentiable Physics for Learning and Control
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.
Differentiable Physics Simulation
Differentiable physics simulation is a powerful family of new techniques that applies gradient-based methods to learning and control of physical systems.
Differentiable Cloth Simulation for Inverse Problems
We propose a differentiable cloth simulator that can be embedded as a layer in deep neural networks.
Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images
We propose a scalable neural network framework to reconstruct the 3D mesh of a human body from multi-view images, in the subspace of the SMPL model.
Ranked #4 on
Multi-view 3D Human Pose Estimation
on MPI-INF-3DHP
3D Human Pose Estimation
Multi-view 3D Human Pose Estimation
Learning-Based Cloth Material Recovery From Video
To extract information about the cloth, our method characterizes both the motion space and the visual appearance of the cloth geometry.
