PaperFlume Accounts · soon

Library · Sim-to-real transfer

0:00

One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation

Zheng Geng, Nan Wang, Shaocong Xu, Chongjie Ye, Bohan Li, Zhaoxi Chen, Sida Peng, Hao Zhao

PMLR v305 5:42 10 chapters CC BY 4.0 · adaptation, not endorsed by the authors Source checked · visual review incomplete

Download the deck .pptx Transcript .txt · cited Captions .vtt

Editable text and shapes, not pictures of slides.

Feedback

Was this worth your five minutes?

We are in beta. Tell us whether it helped — and if something is wrong, tell us that too.

Not rated

Anything else? (optional)

Goes to a person, not a form queue.

Next

Papers near this one.

Walkthroughs

Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning

shares Learning from demonstration

The Sound of Simulation: Learning Multimodal Sim-to-Real Robot Policies with Generative Audio

shares Sim-to-real transfer

Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware

shares Sim-to-real transfer

Short cuts

Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning

shares Learning from demonstration

The Sound of Simulation: Learning Multimodal Sim-to-Real Robot Policies with Generative Audio

shares Sim-to-real transfer

Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware

shares Sim-to-real transfer

Authors

Want one of these for your paper?

Send the PDF and we make the walkthrough, the deck and the reel. It stays private unless you publish it yourself.

Add your paper How it works

01

Something people finishFive minutes of method beats a PDF nobody opens past the abstract.

02

Slides for your next talkAn editable deck built from your own figures, with the narration in the notes.

03

A cut you can postFifty seconds, vertical, for the places a paper link gets ignored.

04

A page that can be foundA cited transcript sits on the page, so the words in your method are searchable.

Based on “One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation” by Zheng Geng, Nan Wang, Shaocong Xu, Chongjie Ye, Bohan Li, Zhaoxi Chen, Sida Peng, Hao Zhao · PMLR v305 · CC BY 4.0 · licence checked 2026-10-24 · Changed from the original: rewritten as a narrated walkthrough with slides, using the paper's own figures. An adaptation, not endorsed by its authors.

Request takedown — we aim to answer within 72 hours.