5 papers
Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition
Nissim Maruani, Peiying Zhang, Siddhartha Chaudhuri +6
We introduce Illustrator's Depth, a novel definition of depth that addresses a key challenge in digital content creation: decomposing flat images into editable, ordered layers. Ins…
tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction
Chen Wang, Hao Tan, Wang Yifan +6
We propose tttLRM, a novel large 3D reconstruction model that leverages a Test-Time Training (TTT) layer to enable long-context, autoregressive 3D reconstruction with linear comput…
Residual Primitive Fitting of 3D Shapes with SuperFrusta
Aditya Ganeshan, Matheus Gadelha, Thibault Groueix +5
We introduce a framework for converting 3D shapes into compact and editable assemblies of analytic primitives, directly addressing the persistent trade-off between reconstruction f…
RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets
Isabella Liu, Zhan Xu, Wang Yifan +5
We present RigAnything, a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints and skeleton topologies and assigning…
ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel Diffusion
Nissim Maruani, Wang Yifan, Matthew Fisher +2
This paper proposes ShapeShifter, a new 3D generative model that learns to synthesize shape variations based on a single reference model. While generative methods for 3D objects ha…