9 citations · 15 across the 8 of their papers we have counts for
8 papers
ArtLang: Structured Language-to-Kinematics Grounding for Articulated 3D Actuation
Sylvia Yuan, Dan Wang, Ravi Ramamoorthi +1
Articulated-object reconstructions recover explicit geometry and kinematics, but their parts often remain semantically anonymous and must be controlled through part indices and num…
ArtMesh: Part-Aware Articulated Mesh Fields with Motion-Consistent Dynamics
Sylvia Yuan, Dan Wang, Ravi Ramamoorthi +1
We present ArtMesh, a mesh-native method for reconstructing articulated objects explicitly as connected triangle meshes with per-part rigid motion from multi-view images in start a…
Revisiting the Perception-Distortion Trade-off with Spatial-Semantic Guided Super-Resolution
Dan Wang, Haiyan Sun, Shan Du +4
Image super-resolution (SR) aims to reconstruct high resolution images with both high perceptual quality and low distortion, but is fundamentally limited by the perception-distorti…
PhysConvex: Physics-Informed 3D Dynamic Convex Radiance Fields for Reconstruction and Simulation
Dan Wang, Xinrui Cui, Serge Belongie +1
Reconstructing and simulating dynamic 3D scenes with both visual realism and physical consistency remains a fundamental challenge. Existing neural representations, such as NeRFs an…
Generalizable Neural Radiance Fields for Novel View Synthesis with Transformer
Dan Wang, Xinrui Cui, Septimiu Salcudean +1
We propose a Transformer-based NeRF (TransNeRF) to learn a generic neural radiance field conditioned on observed-view images for the novel view synthesis task. By contrast, existin…
Multi-view 3D Reconstruction with Transformer
Dan Wang, Xinrui Cui, Xun Chen +5
Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D object reconstruction. Despite the considerable progress, the two core modules of these me…
CHAIN: Concept-harmonized Hierarchical Inference Interpretation of Deep Convolutional Neural Networks
Dan Wang, Xinrui Cui, Z. Jane Wang
With the great success of networks, it witnesses the increasing demand for the interpretation of the internal network mechanism, especially for the net decision-making logic. To ta…
CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks
Xinrui Cui, Dan Wang, Z. Jane Wang
With the widespread applications of deep convolutional neural networks (DCNNs), it becomes increasingly important for DCNNs not only to make accurate predictions but also to explai…