7 papers
Region-Adaptive Generative Compression with Spatially Varying Diffusion Models
Lucas Relic, Roberto Azevedo, Yang Zhang +3
Generative image codecs aim to optimize perceptual quality, producing realistic and detailed reconstructions. However, they often overlook a key property of human vision: our tende…
UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis
Thanh-Tung Le, Tuan Pham, Tung Nguyen +3
Novel view synthesis (NVS) seeks to render photorealistic, 3D-consistent images of a scene from unseen camera poses given only a sparse set of posed views. Existing deterministic n…
GeoDiff: Geometry-Guided Diffusion for Metric Depth Estimation
Tuan Pham, Thanh-Tung Le, Xiaohui Xie +1
We introduce a novel framework for metric depth estimation that enhances pretrained diffusion-based monocular depth estimation (DB-MDE) models with stereo vision guidance. While ex…
Diffusion-Guided Gaussian Splatting for Large-Scale Unconstrained 3D Reconstruction and Novel View Synthesis
Niluthpol Chowdhury Mithun, Tuan Pham, Qiao Wang +6
Recent advancements in 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have achieved impressive results in real-time 3D reconstruction and novel view synthesis. Howe…
One Diffusion to Generate Them All
Duong H. Le, Tuan Pham, Sangho Lee +5
We introduce OneDiffusion, a versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. It enables condit…
Preserving Identity with Variational Score for General-purpose 3D Editing
Duong H. Le, Tuan Pham, Aniruddha Kembhavi +3
We present Piva (Preserving Identity with Variational Score Distillation), a novel optimization-based method for editing images and 3D models based on diffusion models. Specificall…
Neural NeRF Compression
Tuan Pham, Stephan Mandt
Neural Radiance Fields (NeRFs) have emerged as powerful tools for capturing detailed 3D scenes through continuous volumetric representations. Recent NeRFs utilize feature grids to…