[논문리뷰] CAT3D: Create Anything in 3D with Multi-View Diffusion Models
CAT3D 논문 리뷰 (NeurIPS 2024 Oral)
Recent posts
[논문리뷰] TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
TokenFormer 논문 리뷰 (ICLR 2025 Spotlight)
[논문리뷰] SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
SAMURAI 논문 리뷰
[논문리뷰] FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes
FreeSplat 논문 리뷰 (NeurIPS 2024)
[논문리뷰] Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
Depth Pro 논문 리뷰 (ICLR 2025)