2 citations · 4 across the 4 of their papers we have counts for
10 papers · 1 filter
Depth Anything 3: Recovering the Visual Space from Any Views
Haotong Lin, Sili Chen, Junhao Liew +5
We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of…
Trace Anything: Representing Any Video in 4D via Trajectory Fields
Xinhang Liu, Yuxi Xiao, Donny Y. Chen +4
Effective spatio-temporal representation is fundamental to modeling, understanding, and predicting dynamics in videos. The atomic unit of a video, the pixel, traces a continuous 3D…
Revisiting Depth Representations for Feed-Forward 3D Gaussian Splatting
Duochao Shi, Weijie Wang, Donny Y. Chen +4
Depth maps are widely used in feed-forward 3D Gaussian Splatting (3DGS) pipelines by unprojecting them into 3D point clouds for novel view synthesis. This approach offers advantage…
ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
Weijie Wang, Donny Y. Chen, Zeyu Zhang +3
Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene…
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
Yuedong Chen, Chuanxia Zheng, Haofei Xu +4
We introduce MVSplat360, a feed-forward approach for 360° novel view synthesis (NVS) of diverse real-world scenes, using only sparse observations. This setting is inherently ill-po…
MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
Yuedong Chen, Haofei Xu, Chuanxia Zheng +5
We introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we bu…