Aditya Vora retweeted
Please join us tomorrow (Sept. 9) afternoon, 2:00pm, at Malmö Arena Palissad East! I will open the workshop. We have a fantastic line-up of invited speakers, a poster session, and a closing panel session at 4:55pm.
Join us for the Functionality, Articulation & Interaction workshop at #ECCV2026 tomorrow! Our incredible lineup:
- Andrea Vedaldi (@Oxford_VGG)
- Wei-Chiu Ma (@weichiuma)
- Evangelos Kalogerakis (@EvangelosKalog1)
- Angela Dai (@angelaqdai)
- Mikaela Angelina Uy (@mikacuy)
Aditya Vora retweeted
Introducing 𝗔𝗿𝘁𝗶𝘃𝗲𝗿𝘀𝗲 dataset: 5.4K articulated objects for embodied AI 🤖
• 88 categories
• Functional part segmentation + articulation
• Per-part material + mass
• Procedural interiors
#CVPR2026 — Poster 164 Session 2 & ActiVis Workshop
Website + paper below 🧵
Aditya Vora retweeted
We are happy to see these papers accepted to #ACL2026
Looking forward to the conference! 🎊
Excited to share our recent work: Free-Range Gaussians 🥚✨
The core idea: instead of predicting Gaussians on a pixel- or voxel-aligned grid, we let them live freely in 3D space.
🌐 Project: free-range-gaussians.github.…
📝 Paper: arxiv.org/abs/2604.04874
Aditya Vora retweeted
Check out our work on robust 3D reconstruction from 360° captures.
Using casual 360 video capture, ✨FullCircle✨ leverages the full 360° field of view and removes the camera operator from the reconstruction.
Browse our data, code, and webpage.
📢📢📢 Introducing "FullCircle: Effortless 3D Reconstruction from Casual 360° Captures"
TL;DR: 10x faster casual capture with clean reconstructions
Homepage: theialab.github.io/fullcircl…
Code: github.com/theialab/fullcirc…
arXiv: arxiv.org/abs/2603.22572
Led by Yalda Foroutan & Ipek Oztas
HiT: Hierarchical Transformers for Unsupervised 3D Shape Abstraction
- Project: aditya-vora.github.io/HiT/
- Paper: arxiv.org/abs/2510.27088
- Code: github.com/aditya-vora/HiT
We will present HiT at @3DVconf Poster 5-27.
Join us if you are around!
After training, our part embeddings form clear clusters in the embedding space, grouping similar parts from different objects together. This enables the model to learn consistent part correspondences, where visually similar parts naturally lie close to each other.
Our approach produces consistent shape decomposition across multiple levels and in different shape categories, from coarse semantic abstraction to fine detail, all in a fully unsupervised manner.
Done with: @taiyasaki, @lily_goli and @richardzhangsfu
Aditya Vora retweeted
Advances in 4D Representation: Geometry, Motion, and Interaction
Abstract (excerpt)
Instead of offering an exhaustive enumeration of many works, we take a more selective approach by focusing on representative works to highlight both the desirable properties and ensuing challenges of each representation under different computation, application, and data scenarios.
The main take-away message we aim to convey to the readers is how to select and then customize the appropriate 4D representations for their tasks. Organizationally, we separate the 4D representations based on three key pillars: geometry, motion, and interaction. Our discourse will not only encompass the most popular representations of today, such as neural radiance fields (NeRFs) and 3D Gaussian Splatting (3DGS), but also bring attention to relatively under-explored representations in the 4D context, such as structured models and long-range motions.
Throughout our survey, we will reprise the role of large language models (LLMs) and video foundational models (VFMs) in a variety of 4D applications, while steering our discussion towards their current limitations and how they can be addressed. We also provide dedicated coverage on what 4D datasets are currently available, as well as what is lacking, to drive the subfield forward.
Aditya Vora retweeted
ASIA = Adaptive (3D) Segmentation (from) Image Annotations. I’d also called it SAW, Segment Any Way!
Indeed, the most useful and practical segmentation is one that fully adapts to user intents … see technical details below.
📢📢📢 "ASIA" @ #SIGGRAPHAsia2025. 😉
We segment 3D shapes into possibly non-semantic and non-text describable parts using only a few annotated in-the-wild images as references!
👉Project Page: sairajk.github.io/asia/
📄Paper: arxiv.org/abs/2509.24288
Aditya Vora retweeted
📢📢📢 "ASIA" @ #SIGGRAPHAsia2025. 😉
We segment 3D shapes into possibly non-semantic and non-text describable parts using only a few annotated in-the-wild images as references!
👉Project Page: sairajk.github.io/asia/
📄Paper: arxiv.org/abs/2509.24288
Checkout Amir's work on few step image editing with diffusion.
[1/6] Excited to share that our new work, Cora, has been accepted to SIGGRAPH!
🚀We improve fast image editing by leveraging semantic correspondences.
🖌️Cora also gives users flexibility in deciding what to keep or change from the input.
webpage: cora-edit.github.io
Aditya Vora retweeted
Thank you Zhengzhong for sharing! Our code is now avilable at github.com/mingrui-zhao/Swee…
🚨SweepNet: Unsupervised Learning Shape Abstraction via Neural Sweepers [ECCV'24]
🌟𝐏𝐫𝐨𝐣: mingrui-zhao.github.io/Sweep…
🚀𝐀𝐛𝐬: arxiv.org/abs/2407.06305
a novel approach to shape abstraction through sweep surfaces
Aditya Vora retweeted
Introducing Slice3D (#CVPR2024 paper #23, 10:30-12, Arch 4A-E) for single-view 3D reconstruction by first predicting multi-slice images. The key is occlusion revelation, while sidestepping multi-view inconsistency. Trained on single A40 & inf time < 20s.
yizhiwang96.github.io/Slice3…

























