
Overall Score
36
Top 5%
of AI companies
by Microsoft🇺🇸
TRELLIS.2 is an open-source image-to-3D model generator, designed to produce high fidelity textured assets using native 3D VAEs. Its core fe
LIDA is a powerful tool that automates data exploration and generates visualizations and infographics using large language models (LLMs) like ChatGPT and GPT4. It provides a conversational interface for automatic generation of grammar-agnostic visualizations from data. LIDA consists of four modules:...

Overall Score
36
Top 5%
of AI companies
Overall Score
36
GitHub Stars
—
Monthly Visits
1.1M
Community Rating
5.0
LIDA is a powerful tool that automates data exploration and generates visualizations and infographics using large language models (LLMs) like ChatGPT and GPT4. It provides a conversational interface for automatic generation of grammar-agnostic visualizations from data. LIDA consists of four modules:...
Latest detected signals across traffic, developer activity and community mentions.
HN: "Show HN: Microsoft releases Flint, a visualization language for AI ag…"
Indexed by indexator.ai
Referral growth vs previous period
| Keyword | Volume / Mo | CPC |
|---|---|---|
| trellis 2 | 60,510 | $0.81 |
| vibevoice | 60,040 | $1.77 |
| autogen | 48,210 | $2.09 |
| monaco editor | 18,410 | $2.56 |
| vibe voice | 12,460 | $2.69 |
TRELLIS.2 is an open-source image-to-3D model generator, designed to produce high fidelity textured assets using native 3D VAEs. Its core feature makes use of native and compact structured latents, providing both high fidelity and compression capabilities. The method can handle complex structures, i...
Get notified when score, traffic, GitHub or community signals change.
Latest detected signals across traffic, developer activity and community mentions.
HN: "Show HN: Microsoft releases Flint, a visualization language for AI ag…"
Indexed by indexator.ai
Six signals, each 0–100, blended into the overall by their listed weight.
Generated from public signals and search intent.
Each dimension is weighted (traffic 20% · community 25% · growth 15% · momentum 15% · innovation 10%) and combined into the overall.
Referral growth vs previous period
| Keyword | Volume / Mo | CPC |
|---|---|---|
| trellis 2 | 60,510 | $0.81 |
| vibevoice | 60,040 | $1.77 |
| autogen | 48,210 | $2.09 |
| monaco editor | 18,410 | $2.56 |
| vibe voice | 12,460 | $2.69 |
Saves
34.7K
Rating
5.0
1 review
Views
1.1K
Category
3D objects
Pros · 33
Cons · 10
Rating distribution
Release history · 1+
Initial release of TRELLIS 2.
Q&A · 22
TRELLIS.2 is an open-source image-to-3D model generator, designed to produce high fidelity textured assets using native 3D VAEs. Its core feature makes use of native and compact structured latents for providing both high fidelity and compression capabilities. This tool can handle complex structures, including open surfaces, non-manifold geometry, and enclosed interior structures, thus overcoming the limitations of iso-surface fields. It's a research project with Responsible AI considerations factored into all stages of its development.
TRELLIS.2 generates 3D models from images through a process that begins with an Instant Bidirectional Conversion that transforms meshes into a new representation termed O-Voxel. The Sparse Compression VAE then encodes these voxels into a compact Structured Latent space. The result is a highly compact representation of a fully textured 3D asset with negligible perceptual degradation.
Key features of TRELLIS.2 include the ability to handle complex structures, model arbitrary surface attributes such as base colour, roughness, metallic, and opacity, and optimize pre and post-processing of data for training and inference. Additional features include the utilization of 'O-Voxel', a novel 'field-free' sparse voxel structure to encode detailed geometry and complex appearance simultaneously, and a Sparse Compression VAE component for efficient voxel data compression.
TRELLIS.2 can handle complex structures including open surfaces, non-manifold geometry, and enclosed interior structures.
TRELLIS.2 handles non-manifold geometry by using a flexible dual grids representation within the O-Voxel structure. This methodology allows it to handle arbitrary topologies while preserving sharp edges.
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Top 10 in Data & Infrastructure.
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