Lakonlab
Official implementation of AsymFlow, pi-Flow, GMFlow
Last verified:
What is Lakonlab?
Lakonlab — his page is Hansheng Chen's academic research portfolio from Stanford University, not a tool or product website. It displays his publications in diffusion models, video generation, 3D generation, and 3D vision. The page serves as a showcase of research papers with authors, venues, and brief summaries. It is intended for researchers, academics, and anyone interested in Hansheng Chen's work on generative models. There is no downloadable tool, software, or product with features, pricing, or user-facing functionality.
Key content includes papers on Asymmetric Flow Models, pi-Flow, Gaussian Mixture Flow Matching, 3D-Adapter, GRM, Zero123++, EPro-PnP, and other research in machine learning and computer vision. The website links to his CV, publications, and engineering projects.
This is an academic portfolio page for a graduate student at Stanford working with the Guibas Lab, not a commercial or open-source tool with pros/cons or pricing tiers.
Lakonlab pricing
Pricing model: Freemium
This is an academic research portfolio, not a product or tool. There is no pricing, free tier, or paid plans. All research papers are publicly available through arXiv, conference proceedings, or university repositories at no cost.
Lakonlab pros
- Showcases cutting-edge research in diffusion and flow models
- Includes papers published at top venues like ICLR, ICML, CVPR, ECCV, SIGGRAPH Asia
- Provides brief summaries of each research paper
- Lists all authors and affiliations for each publication
- Shows research focus areas clearly (Diffusion, Video, 3D Generation, 3D Vision)
- Links to related project pages and GitHub repositories
- Includes award recognition (Best Student Paper for EPro-PnP)
- Shows state-of-the-art results (e.g., #1 on HPSv3 benchmark, 1.57 FID on ImageNet)
- Demonstrates collaboration with leading researchers in the field
- Provides contact information (email)
- Includes links to Google Scholar profile with citation metrics
- Shows progression of research from undergraduate to graduate work
- Covers multiple important AI subfields comprehensively
- Pages are well-organized by research category
- Includes technical details like solver types and model architectures
Lakonlab cons
- Not a tool or product — it is an academic portfolio page
- No downloadable software or code available directly on this page
- No pricing information since there is no product
- No user-facing features or functionality
- No FAQs section on the website
- Research papers are on arXiv/conferences, not hosted directly
- Some projects link to external GitHub pages not always updated
- No interactive demos or toy implementations shown
Frequently asked questions about Lakonlab
What is Lakonlab?
Lakonlab is not a tool or product on this website. The website belongs to Hansheng Chen, a graduate student at Stanford University in the Guibas Lab. 'Lakonik' appears to be his research lab/portfolio name (lakonik.github.io), but there is no product called Lakonlab.
Can I download any tools from this page?
No, this is an academic research portfolio showing publications. Some papers may have code on GitHub (like hanchenresearch), but this specific page does not host downloadable tools.
What is Hansheng Chen's research focus?
His research focuses on the fundamentals of generative models, with current emphasis on diffusion and flow-based models. Previously he worked on 3D generation and 3D vision, including object detection and pose estimation.
Where is Hansheng Chen based?
He is a graduate student at Stanford University in the Guibas Lab, with email [email protected].
What are his most notable papers?
Notable papers include Asymmetric Flow Models (arXiv 2026, #1 on HPSv3), pi-Flow (ICLR 2026), GMFlow (ICML 2025), 3D-Adapter (arXiv 2024), GRM (ECCV 2024), Zero123++ (2023), and EPro-PnP (CVPR 2022 Best Student Paper).
Is there a free tier for using his models?
No pricing exists because these are research papers, not commercial products. The research is publicly available, but specific model availability depends on each project's GitHub repository or Hugging Face page.
What conferences has he published at?
He has published at ICLR 2026, ICML 2025 and 2026, CVPR 2021, 2022, and 2025, ECCV 2024, ICCV 2023, SIGGRAPH Asia 2025, and arXiv preprints.
What is 3D-Adapter?
3D-Adapter is a plug-in module that enables high-quality 3D generation by attaching a 3D feedback module to a base image diffusion model for enhanced geometry consistency. It infuses 3D geometry awareness into pretrained image diffusion models.
What is EPro-PnP?
EPro-PnP is a generalized end-to-end probabilistic Perspective-n-Points method for monocular object pose estimation. It outputs pose distribution with differentiable probability density, won Best Student Paper at CVPR 2022, and was later updated in TPAMI 2024 with improved results.
How can I access his code?
Code for his projects may be available on GitHub under hanchenresearch or individual project pages like lakonik.github.io. Some papers also have links to Hugging Face (Lakonik user profile).