mlsanityData to 7 Oct 2026

Seed3D 2.0: Advancing High-Fidelity Simulation-Ready 3D Content Generation

arXiv 2605.13862 ↗First posted 22 Apr 2026cs.GR cs.CV eess.IVcited by 4

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Abstract (arXiv)

We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulation-ready capabilities, and application coverage. For geometry, a coarse-to-fine two-stage pipeline decouples global structure learning from high-frequency detail recovery, while a locality-aware VAE achieves higher spatial compression and more efficient decoding. For texture and material generation, we replace the cascaded pipeline of Seed3D 1.0 with a unified PBR model that directly generates multi-view albedo and metallic-roughness maps, enhanced by Mixture-of-Experts scaling and VLM-based semantic conditioning for improved material precision and visual fidelity. Beyond single-object generation, Seed3D 2.0 introduces a simulation-ready model suite comprising scene layout planning, part-aware decomposition, and training-free articulation generation, enabling coherent scene construction and part-level physical interaction across physics and graphics engines. A large-scale human preference study against five recent commercial models shows that Seed3D 2.0 achieves consistent win rates of 69.0% to 89.9% in textured 3D asset generation. Seed3D 2.0 is available on https://exp.volcengine.com/ark/vision?_vtm_=0.0.c70961.d701978.0&mode=vision&modelId=doubao-seed3d-2-0-260328&tab=Gen3D

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 22 Apr 2026.

Builds on

16 of 22 references.

References · 16

  1. 2022Flow Matching for Generative Modelingcited by 5,426
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  3. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 781
  4. 2023Point Transformer V3: Simpler, Faster, Strongercited by 579
  5. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 449
  6. 2025Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generationcited by 342
  7. 2025TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Modelscited by 204
  8. 2025Native and Compact Structured Latents for 3D Generationcited by 157
  9. 2023PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objectscited by 115
  10. 2025Sonata: Self-Supervised Learning of Reliable Point Representationscited by 108
  11. 2024Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoderscited by 93
  12. 2025PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformerscited by 78
4 more
  1. 2025Puppeteer: Rig and Animate Your 3D Modelscited by 41
  2. 2025LATTICE: Democratize High-Fidelity 3D Generation at Scalecited by 41
  3. 2025AutoPartGen: Autogressive 3D Part Generation and Discoverycited by 33
  4. 2024DragAPart: Learning a Part-Level Motion Prior for Articulated Objectscited by 32

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

Similar, not cited

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  1. 2026Gen2Physics: Grounding Generated 3D Meshes in Physics via Multi-View Material Decomposition
  2. 2025Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets
  3. 2023DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
  4. 20243DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
  5. 2025Self-Evolving 3D Scene Generation from a Single Image
  6. 2026From Visual Synthesis to Interactive Worlds: Toward Production-Ready 3D Asset Generation
  7. 2024Structured 3D Latents for Scalable and Versatile 3D Generation
  8. 2023HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image