CVPR2023_Acceptedpapers_Classification

CVPR2023 Accepted Papers List

Part 1: Learning Optical Expansion from Scale Matching ---- Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Informationn

Part 2: CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching ---- Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark

Part 3: Vision Transformers are Good Mask Auto-Labelers ---- ECON: Explicit Clothed humans Optimized via Normal integration

Part 4: Zero-shot Generative Model Adaptation via Image-specific Prompt Learning ---- ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization

Part 5: Token Boosting for Robust Self-Supervised Visual Transformer Pre-training ---- Mask-guided Matting in the Wild

Neural Parameterization

• DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization

Appearance Modeling & BRDFs

• NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images
• NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
• NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects
• RelightableHands: Efficient Neural Relighting of Articulated Hand Models
• Multi-View Azimuth Stereo via Tangent Space Consistency
• VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization

Faster training

voxel/sparse-voxel/octree-based method

hash-table/triplane-based method

• InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds
• Compressing Volumetric Radiance Fields to 1 MB

3D Shape Generative Models

Reconstruction/driven from Monocular video based-NeRF

• MonoAvatar: Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos

Sparse views

• DINER: Depth-aware Image-based NEural Radiance fields
• FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization