code for the paper: Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery
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Updated
Jun 11, 2024 - Python
code for the paper: Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery
[CVPR 2024] Memory-based Adapters for Online 3D Scene Perception
Official implementation of CVPR 2024 PromptAD: Learning Prompts with Only Normal Samples for Few-Shot Anomaly Detection
Official Code for 'TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshes' (CVPR 2024)
PyTorch implementation of the paper : Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation.
[CVPR 2024] Code for "Improved Visual Grounding through Self-Consistent Explanations".
This is the project page for our CVPR2024 paper "MVCPS-NeuS: Multi-view Constrained Photometric Stereo for Neural Surface Reconstruction."
[CVPR 2024] The official implementation of paper "synthesize, diagnose, and optimize: towards fine-grained vision-language understanding"
[CVPR 2024] AMUSE: Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion
[CVPR 2024] Official implementation of the paper "ReGenNet: Towards Human Action-Reaction Synthesis"
Official PyTorch Implementation for "Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection" in CVPR 2024
[CVPR 2024] REACTO: Reconstructing Articulated Objects from a Single Video
CVPR 2024: Robust Depth Enhancement via Polarization Prompt Fusion Tuning
[CVPR2024] The official implementation of "MoCha-Stereo: Motif Channel Attention Network for Stereo Matching”.
[CVPR 2024] Asymmetric Masked Distillation for Pre-Training Small Foundation Models
[CVPR 2024] OHTA: One-shot Hand Avatar via Data-driven Implicit Priors
Code repo for the paper "Building Optimal Neural Architectures using Interpretable Knowledge", accepted to CVPR'24.
Implementation of "Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring". (Zhang et al., CVPR 2024)
The official code of "CSTA: CNN-based Spatiotemporal Attention for Video Summarization"
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