SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation
- URL: http://arxiv.org/abs/2606.04108v1
- Date: Tue, 02 Jun 2026 18:11:41 GMT
- Title: SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation
- Abstract summary: We present SymTRELLIS, a method that enforces arbitrary finite point group symmetries during the flow-based 3D generation of TRELLIS.2.<n>On a curated benchmark of 266 strictly symmetric objects, SymTRELLIS substantially reduces all symmetry error metrics compared to TRELLIS.2, Hunyuan3D-2.1, and TripoSG.
- Score: 10.616258272621836
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Single-view 3D generative models have achieved impressive visual quality, yet they are not designed to satisfy structural or functional requirements, and in practice, often fall short. Symmetry is one such requirement: violations, even subtle ones, on symmetry can render a model physically unusable. We present SymTRELLIS, a method that enforces arbitrary finite point group symmetries (rotational, reflectional, and polyhedral) during the flow-based 3D generation of TRELLIS.2, without retraining the underlying VAE or flow model. Our key idea is to approximate the latent-space action of spatial transformations as a learned linear operator on voxel latents, implemented as a lightweight spatial-transform latent mapper trained on generic, non-symmetric 3D data. At generation time, we enforce symmetry by averaging predicted flow velocities across all symmetry-equivalent transformations at each ODE step, a process we call velocity symmetrization. The symmetry specification can be estimated automatically from an initial TRELLIS.2 generation or supplied by the user, enabling deliberate fold manipulation beyond what the input image suggests. On a curated benchmark of 266 strictly symmetric objects spanning 2- to 20-fold rotations and polyhedral symmetry groups, SymTRELLIS substantially reduces all symmetry error metrics compared to TRELLIS.2, Hunyuan3D-2.1, and TripoSG, while maintaining reconstruction accuracy comparable to the base model.
Related papers
- Planar Symmetric Pattern Generation [40.32465897894085]
We propose a symmetrization framework for arbitrary planar groups.<n>Our method transforms any 2D continuous representation into a symmetric one while preserving continuity.
arXiv Detail & Related papers (2026-06-01T11:01:48Z) - Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers [3.433766572511366]
A striking geometric disparity has long persisted in the practice of deep learning.<n>We introduce a symmetry-compatible principle for design: the gradient update rule should be equivariant under the parameter group acting on the corresponding weight block.
arXiv Detail & Related papers (2026-05-18T09:17:26Z) - Aligning Network Equivariance with Data Symmetry: A Theoretical Framework and Adaptive Approach for Image Restoration [71.51826063983434]
Image restoration is an inherently ill posed inverse problem.<n>We use non strict symmetry as a constraint to formulate the restoration inverse problem.<n>We show that the equivariance error of the optimal restoration operator is strictly bounded by the data symmetry error and the discretization mesh size.
arXiv Detail & Related papers (2026-05-13T16:22:19Z) - SymDrift: One-Shot Generative Modeling under Symmetries [19.741742157466888]
We show that drifting models face a symmetry-specific challenge, since an equivariant generator does not generally produce the same drifting field as the one obtained from the symmetrized target distribution.<n>We introduce two complementary strategies: (i) a symmetrized drift in coordinate space based on optimal alignment, and (ii) a $G$-invariant embedding that removes symmetry ambiguity by construction.<n>By enabling one-shot inference, SymDrift reduces computational overhead by up to 40$times$ compared to existing baselines.
arXiv Detail & Related papers (2026-05-07T12:38:44Z) - Symmetry-Reduced Physics-Informed Learning of Tensegrity Dynamics [33.52984112921709]
Most existing physics-informed neural network (PINN) approaches for tensegrity dynamics do not explicitly exploit intrinsic geometric symmetries.<n>We propose a symmetry-reduced physics-informed neural network (SymPINN) framework that embeds group-theory-based symmetry directly into both the solution expression and the neural network architecture.
arXiv Detail & Related papers (2026-03-18T15:23:23Z) - Generalized Linear Mode Connectivity for Transformers [87.32299363530996]
A striking phenomenon is linear mode connectivity (LMC), where independently trained models can be connected by low- or zero-loss paths.<n>Prior work has predominantly focused on neuron re-ordering through permutations, but such approaches are limited in scope.<n>We introduce a unified framework that captures four symmetry classes: permutations, semi-permutations, transformations, and general invertible maps.<n>This generalization enables, for the first time, the discovery of low- and zero-barrier linear paths between independently trained Vision Transformers and GPT-2 models.
arXiv Detail & Related papers (2025-06-28T01:46:36Z) - RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization [26.386588932734345]
We introduce RECON, a class-pose agnostic $textitcanonical orientation normalization$ that corrects arbitrary canonicals via a simple right-multiplication.<n>We demonstrate results on 2D image benchmarks and --for the first time-- extend symmetry discovery to 3D groups.
arXiv Detail & Related papers (2025-05-19T16:10:23Z) - Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks [79.16635054977068]
We present a new class of equivariant neural networks, dubbed Lattice-Equivariant Neural Networks (LENNs)
Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators.
Our work opens towards practical utilization of machine learning-augmented Lattice Boltzmann CFD in real-world simulations.
arXiv Detail & Related papers (2024-05-22T17:23:15Z) - Learning Layer-wise Equivariances Automatically using Gradients [66.81218780702125]
Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance.
symmetries provide fixed hard constraints on the functions a network can represent, need to be specified in advance, and can not be adapted.
Our goal is to allow flexible symmetry constraints that can automatically be learned from data using gradients.
arXiv Detail & Related papers (2023-10-09T20:22:43Z) - Latent Space Symmetry Discovery [31.28537696897416]
We propose a novel generative model, Latent LieGAN, which can discover symmetries of nonlinear group actions.
We show that our model can express nonlinear symmetries under some conditions about the group action.
LaLiGAN also results in a well-structured latent space that is useful for downstream tasks including equation discovery and long-term forecasting.
arXiv Detail & Related papers (2023-09-29T19:33:01Z) - Oracle-Preserving Latent Flows [58.720142291102135]
We develop a methodology for the simultaneous discovery of multiple nontrivial continuous symmetries across an entire labelled dataset.
The symmetry transformations and the corresponding generators are modeled with fully connected neural networks trained with a specially constructed loss function.
The two new elements in this work are the use of a reduced-dimensionality latent space and the generalization to transformations invariant with respect to high-dimensional oracles.
arXiv Detail & Related papers (2023-02-02T00:13:32Z) - A Model for Multi-View Residual Covariances based on Perspective
Deformation [88.21738020902411]
We derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups.
We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment.
arXiv Detail & Related papers (2022-02-01T21:21:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.