seed3d

seed3d 1.0 is ByteDance’s diffusion-transformer breakthrough for turning a single RGB image into a closed-manifold, PBR-textured 3D asset that is simulation-ready for robotics, gaming, and XR pipelines, as detailed in the official Seed3D launch blog.

1.5B

Parameters powering the diffusion-transformer core

3D

Closed-manifold meshes with UV textures & PBR maps

<1

Input image needed to synthesize detailed assets

Explore seed3d workflow View seed3d comparisons

seed3d Single Image to Simulation-Ready

The seed3d pipeline fuses a dual encoder, latent diffusion transformer, multi-view texture synthesis, and PBR material estimation into one end-to-end system. Consistent geometry, crisp textures, and realistic BRDFs arrive in minutes—no manual cleanup.

Latent shape tokens refined via diffusion guarantees closed surfaces.

Multi-view conditioning keeps albedo consistent across renderings.

Two-stream attention disentangles albedo vs. metalness/roughness.

What is seed3d 1.0?

seed3d 1.0 is ByteDance’s 2025 single-image-to-3D diffusion-transformer model that outputs detailed explicit meshes. It blends an image encoder with a latent 3D VAE to convert a single RGB input into shape tokens, then iteratively denoises them into high-fidelity geometry before decoding through a mesh generator to ensure watertight topology suitable for physics simulations.

Unlike multi-view reconstruction pipelines, seed3d maintains deterministic feed-forward performance. It integrates multi-view texture synthesis for aligning albedo, metalness, and roughness across viewpoints, all while preserving small typographic and mechanical details.

Every seed3d output includes UV-mapped textures, PBR maps (albedo, metalness, roughness), and consistent scale metadata that downstream platforms like NVIDIA Isaac Sim can utilize to infer mass and friction, enabling immediate embodied AI experimentation.

Diffusion Transformer

All-attention diffusion backbone shared across geometry, texture, and materials, orchestrated with timestep shifting for growing token sets.

End-to-End Pipeline

From RGB input to render-ready mesh in a single pass, removing the need for separate meshing or UV unwrapping stages.

Simulation-Ready Assets

Closed manifolds, consistent scales, and dense textures designed for robotics, AR/VR twins, and photorealistic visualization.

seed3d architecture insights

seed3d diffusion-transformer pipeline

The seed3d architecture begins with a dual encoder: convolutional stages capture local appearance from the input image, while a transformer encoder embeds global context. A 3D variational autoencoder compresses geometry into latent tokens. These tokens enter a diffusion transformer stack—multiple self-attention layers conditioned on image features—where timestep shifting techniques manage the expanding token sequence as multi-view conditioning accumulates, as explained in ByteDance’s architecture deep dive.

Geometry decoding relies on the VAE decoder to recover closed-manifold meshes. Texture synthesis is handled by an MMDiT branch that fuses the reference photo with rendered mesh views, ensuring consistent colors and fine-grained texture alignment. The PBR estimation component splits queries for albedo and metalness/roughness, enabling precise material decomposition without cross-channel contamination.

During inference, seed3d denoises from a learned latent schedule, producing explicit vertices, faces, UV maps, and PBR textures in a deterministic number of steps. The holistic design eliminates iterative NeRF optimization and lowers latency while preserving photorealistic fidelity.

Key Architecture Traits

  • Two-stream material attention for disentangled reflectance modeling.
  • Optimized positional encodings support multi-view texture generation.
  • Latent diffusion on shape tokens preserves manifold integrity.

seed3d outline visualization

Geometry • Texture • Materials

Geometry Tokens Texture Branch Material Streams

This minimal line graphic underscores how seed3d orchestrates geometry, texture, and material pathways inside one denoising curve, balancing major transformations (large circles) with fine adjustments (small inflections).

seed3d feature galaxy

Core seed3d capabilities

  • Closed-manifold mesh reconstruction with preserved topology and edge sharpness.
  • Multi-view texture synthesis that keeps text, logos, and mechanical engravings crisp.
  • PBR material estimation delivering accurate albedo, metalness, and roughness maps.
  • Latent diffusion scheduling for fast inference without iterative NeRF optimization.
  • Scale-aware outputs driven by a visual-language model for real-world size alignment.

seed3d differentiators

  • Deterministic pipeline that produces ready-to-render meshes from one photo.
  • Timestep shifting and custom positional encodings for multi-view coherence.
  • Two-stream attention that disentangles BRDF channels for realistic lighting response.
  • Large heterogeneous training corpus spanning synthetic and scanned assets.
  • Integration with simulation toolchains like NVIDIA Isaac Sim via USD-ready exports.

How to use seed3d effectively

seed3d API workflow

  1. Capture or upload a calibrated RGB image of the target asset.
  2. Send the image to the ByteDance seed3d cloud API hosted on VolcEngine.
  3. Seed3d’s encoder and diffusion transformer generate latent shape tokens.
  4. Download the resulting mesh, UV textures, and PBR material maps.
  5. Import the asset into robotics simulators, game engines, or digital twin platforms.

The deterministic nature of seed3d means each request yields consistent outputs without manual tuning. For robotics, the service emits size metadata so platforms like Isaac Sim can infer mass and friction automatically.

seed3d pipeline tips

  • Use high-resolution photos to maximize fine detail retention.
  • Leverage multi-light captures if possible—seed3d’s texture branch benefits from varied shading cues.
  • Integrate with Isaac Sim or Omniverse to auto-generate collision meshes and physics parameters.
  • Curate input prompts with view-conditioned context when embedding into broader pipelines.
  • Store PBR outputs in standardized formats (.png, .exr) for real-time shading workflows.

seed3d deployment checklist

Robotics

Scale objects with the companion VLM, then push to Isaac Sim for grasping benchmarks.

Digital Twins

Leverage consistent PBR maps for photorealistic lighting in AR/VR experiences.

Content Creation

Replace manual modeling with generated assets for rapid prototyping in games or visualization.

seed3d performance signals

ByteDance reports that seed3d 1.0 achieves state-of-the-art quality across geometry, texture, and material fidelity benchmarks. Compared with Tencent’s Hunyuan3D-2.1, seed3d’s 1.5-billion-parameter design preserves sharper edges, legible text, and intricate mechanical components while consuming fewer parameters than the 3-billion-parameter baseline, a result highlighted by TechNode’s Seed3D coverage.

In human evaluations involving 14 annotators and 43 test images, seed3d ranked highest for geometric accuracy, texture sharpness, material realism, visual clarity, and detail richness. Users reported crisper facial features and fabric patterns relative to competing outputs.

Although ByteDance has not released explicit numeric F-scores or IoU metrics, the qualitative evidence—showing faithful reproduction of “steampunk clock” engravings and improved highlights under strong lighting—underscores the model’s photorealistic performance.

seed3d evaluation highlights

  • Superior geometric fidelity versus open and closed-source alternatives.
  • Consistent multi-view textures preserving micro-detail and typography.
  • Realistic PBR maps that sustain highlights and subsurface cues.

seed3d simulation impact

  • Robotics tasks gain precise contact modeling for grasping and manipulation.
  • Embodied AI benefits from dense scene diversity and real-time physics feedback.
  • Multi-modal observations (RGB, depth, materials) accelerate VLA benchmarks.

seed3d applications

Robotics & Embodied AI

Seed3D assets plug into NVIDIA Isaac Sim with minimal edits, enabling grasping and multi-object manipulation scenarios. The closed-manifold meshes create accurate collision geometry so planners receive faithful contact feedback.

AR/VR & Digital Twins

Photorealistic PBR maps ensure virtual objects behave like real ones under varied lighting. This supports immersive digital twins aligned with NVIDIA’s Omniverse SimReady philosophy.

Gaming & Visualization

From ancient architecture scenes to intricate props, seed3d accelerates asset production for interactive content, e-commerce previews, and cultural heritage digitization.

seed3d dataset & training

Seed3D training leverages a massive proprietary dataset of synthetic and scanned 3D assets. ByteDance normalizes coordinate systems, standardizes file formats, removes duplicates, and reorients poses before surfacing each mesh to guarantee watertight geometry. Semantic labels enrich the corpus, while multi-view renderings pair each asset with consistent imagery, according to industry reports from AIBASE.

Geometry diffusion is trained on latent shape encodings, and texture diffusion learns from rendered multi-view image pairs plus the input photo. Scarcity of real-world PBR captures is mitigated by teaching material decomposition via the multi-view renderings, allowing seed3d to generalize across categories far beyond public benchmarks.

Although dataset size remains undisclosed, ByteDance emphasizes that it surpasses prior public collections in scale and diversity, directly enabling seed3d’s robust zero-shot performance.

seed3d competitive comparison

Model (Year) Input Output 3D Format Approach Availability
ByteDance seed3d (2025) Single RGB Image Textured Mesh + PBR Explicit Mesh Diffusion Transformer on latent 3D tokens API/demo; technical report
Google DreamFusion (2022) Text Prompt Relightable NeRF Implicit NeRF Score Distillation with 2D diffusion prior Code/demo (iterative)
Columbia Zero-1-to-3 (2023) Single Image Multi-view Images Implicit Views Viewpoint-conditioned diffusion Code released
Tencent Hunyuan3D-2.1 (2024) Image/Text Textured Mesh Explicit/Implicit hybrid Large diffusion model pipeline Limited (proprietary)
NVIDIA Magic3D (2022) Text Mesh Latent + NeRF Diffusion + SDS optimization Open code/models
OpenAI Point-E (2023) Text Point Cloud / Mesh Point diffusion Latent diffusion + SDS Open models

seed3d access & availability

ByteDance provides documentation, a technical report, and demo access for seed3d 1.0 through its cloud platform. While the full model weights remain proprietary, researchers can experiment via the hosted API and study the released materials to replicate aspects of the pipeline through the Seed3D portal.

  • Technical report and blog deep dives at the Seed team portal.
  • Interactive demos available on VolcEngine for image-to-3D generation trials.
  • GitHub organization hosts research code references (without pretrained checkpoints).

seed3d faq

seed3d sources

Explore the official reports and analyses informing this seed3d overview: