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3DGen: Triplane Latent Diffusion for Textured Mesh Generation

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arxiv 2303.05371 v2 pith:S7NZL7ZT submitted 2023-03-09 cs.CV cs.GR

Anchit Gupta , Wenhan Xiong , Yixin Nie , Ian Jones , Barlas Oğuz This is my paper · ORCID
classification cs.CVcs.GR
keywords generationqualitytriplanediffusionlatenttexturedconditionalmesh
verification ladder T0 review T1 audit T2 compute T3 formal
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Latent diffusion models for image generation have crossed a quality threshold which enabled them to achieve mass adoption. Recently, a series of works have made advancements towards replicating this success in the 3D domain, introducing techniques such as point cloud VAE, triplane representation, neural implicit surfaces and differentiable rendering based training. We take another step along this direction, combining these developments in a two-step pipeline consisting of 1) a triplane VAE which can learn latent representations of textured meshes and 2) a conditional diffusion model which generates the triplane features. For the first time this architecture allows conditional and unconditional generation of high quality textured or untextured 3D meshes across multiple diverse categories in a few seconds on a single GPU. It outperforms previous work substantially on image-conditioned and unconditional generation on mesh quality as well as texture generation. Furthermore, we demonstrate the scalability of our model to large datasets for increased quality and diversity. We will release our code and trained models.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Global Latents: Chunk-Based Sparse Grid VAE for Scalable 3D Modeling

    cs.CV 2026-08 conditional novelty 7.0 of 10

    ChunkVAE shows that a sparse-grid 3D VAE can be trained and run on local chunks with independent encoder and decoder partitions, then stitched into a global latent with small quality loss.

  2. LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

    cs.CV 2026-04 conditional novelty 6.0 of 10

    Scaling transformer context with sparse attention and 3D-aware block routing improves feed-forward 3D reconstruction and inverse rendering, closing much of the quality gap with dense-view optimization.

  3. Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework

    cs.GR 2025-09 conditional novelty 6.0 of 10

    SDF grids reconstruct best, Dual Octrees score best on automatic generation metrics, but users prefer SDF output, and reconstruction plus compression errors make up a large share of generation error.

  4. MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Mesh

    cs.GR 2025-08 unverdicted novelty 6.0 of 10

    MeshLLM improves LLM-based 3D mesh understanding and generation through primitive decomposition, a 1500k+ sample dataset, and topology-focused training strategies.

  5. Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A video-to-4D model that encodes mesh animations into compact Gaussian variation latents and diffuses them conditioned on the video and a canonical Gaussian splat.

  6. LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving

    cs.CV 2025-07 conditional novelty 6.0 of 10

    LoomNet generates consistent multi-view images from a single input by fusing per-view latent predictions onto shared triplane features before rendering.

  7. SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SegmentDreamer reformulates score distillation as segmented consistency trajectory distillation, partitioning the diffusion ODE into sub-trajectories to balance conditional and unconditional guidance in text-to-3D generation.

  8. Efficient Part-level 3D Object Generation via Dual Volume Packing

    cs.CV 2025-06 conditional novelty 6.0 of 10

    From a single image, a 3D latent diffusion model generates all parts of an object at once by packing the part structure into two non-overlapping volumes.

  9. Few-step Flow for 3D Generation via Marginal-Data Transport Distillation

    cs.CV 2025-09 conditional novelty 5.0 of 10

    MDT-dist distills a pretrained 3D flow model into a 1-2 step generator using velocity matching plus velocity distillation, cutting TRELLIS inference from 6.1s to 0.68s while approximately preserving generation quality.

  10. RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation

    cs.LG 2025-08 unverdicted novelty 4.0 of 10

    RelMap combines GNN-based imputation with spatial interpolation and uncertainty-aware heatmaps for spatiotemporal sensor data.

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