youngjoongkwon

Assistant Professor @ Emory CS

[youngjoongkwon @youngjoongkwon]

Call for Papers : MoCha @ ECCV 2026
Human Motion Challenges in Real-World and Clinical Settings

Two tracks:
- Proceedings track : deadline July 1
- Non-proceedings track : deadline July 23

Details: https://t.co/kq89TD8rax

[Jon Barron @jon_barron]

I'll be giving a "exactly how bitter lesson'ed is all of 3D computer vision?" talk at the Bitter Lessons CVPR workshop tomorrow at 10:30am, Room 3A-3D. Here's a slide with the overall thesis.

[Babak Taati @Babak_Taati]

Paper and challenge submissions are now open for MoCHa @ ECCV 2026, our workshop on Human Motion Challenges in Real-World and Clinical Settings!

Challenge: https://t.co/0zEaogl5mR
Workshop: https://t.co/TsphgKcWMR

Cash prizes + best paper award sponsored by @MachineMedicine.

[Babak Taati @Babak_Taati]

Call for Papers! #ECCV2026 Workshop and Challenge on Human Motion Challenges in Real-World and Clinical Settings.

🌐Website: https://t.co/TsphgKcWMR

best paper award and cash prizes for top 2 challenge teams generously sponsored by @MachineMedicine

[Tobias Kirschstein @TobiasKirschst1]

There are 2 weeks left to submit your method to the NeRSemble 3D Head Avatar benchmark! Deadline May 26th.

🏆The winners of the workshop challenges get a GPU prize and an oral presentation slot at our @CVPR workshop.

👉https://t.co/5jCMaf3iMu

#CVPRWorkshop #CVPR2026

[youngjoongkwon @youngjoongkwon]

@marc_habermann Huge congratulations Marc!!!!

[Sida Peng @pengsida]

The training code for InfiniDepth is now open-source.
Feel free to use our framework to train a monocular depth estimation model as well as a depth sensor augmentation model on your own data.
https://t.co/cYgjkFckdh

[MrNeRF @janusch_patas]

[SIGGRAPH '25] Echoes of the Coliseum: Towards 3D Live streaming of Sports Events

TL;DR: "..., an end-to-end streaming framework, which takes multi-view video streams of sports events as input and reconstructs the dynamic 3D scene to allow viewers to watch from any novel view with interactive frame rates."

Tip: Watch the YouTube Video linked below and visit the project!

Contributions:
1. A multi-view dynamic dataset of sports events featuring complex human motions and comprehensive ground-truth annotations.

2. A 4D reconstruction challenge designed to establish a new benchmark for 4D reconstruction, addressing limitations in current datasets like N3DV and Google Immersive, which have limited camera counts and lack true 360° coverage.

3. A scalable method (LiveSplats) that enables interactive framerate 4D scene reconstruction by simplifying the task to an implementation problem.

4. This work is the first in a planned series, with future expansions to include broader event coverage, increased scene complexity, and improved algorithms to make immersive, live sports viewing feasible.

[Gordon Wetzstein @GordonWetzstein]

High-resolution image and video generation is hitting a wall because attention in DiTs scales quadratically with token count. But does every pixel need to be in full resolution?

Introducing Foveated Diffusion: a new approach for efficient diffusion-based generation that allocates compute where it matters most.

1/7🧵

[Sida Peng @pengsida]

Excited to share our work InfiniDepth (CVPR 2026) — casting monocular depth estimation as a neural implicit field, which enables:
🔍 Arbitrary-Resolution
📐 Accurate Metric Depth
📷 Large-View Novel View Synthesis
Feel free to try our code: https://t.co/389WULpzhC

[MrNeRF @janusch_patas]

RetimeGS Continuous-Time Reconstruction of 4D Gaussian Splatting

TL;DR: A new 4D Gaussian Splatting representation designed to render at arbitrary timesteps, even when the input contains relatively large inter-frame motion.

Abstract (excerpt):
Most existing 4D Gaussian Splatting (4DGS) methods overfit at discrete frame indices but struggle to represent continuous-time frames, leading to ghosting artifacts when interpolating between timestamps.

We identify this limitation as a form of temporal aliasing and propose RetimeGS, a simple yet effective 4DGS representation that explicitly defines the temporal behavior of the 3D Gaussian and mitigates temporal aliasing.

To achieve smooth and consistent interpolation, we incorporate optical flow-guided initialization and supervision, triple-rendering supervision, and other targeted strategies.

Together, these components enable ghost-free, temporally coherent rendering even under large motions.

[youngjoongkwon @youngjoongkwon]

Call for Papers : PhysHuman @ CVPR 2026

PhysHuman is a workshop dedicated to physically grounded human perception and modeling.

📄 Full papers (8p, archival)
📄 Short papers (4p, non-archival)
⏳ Submission deadline: March 13, 2026 (23:59 AoE)

🔗https://t.co/RNfJ94feLv

[youngjoongkwon @youngjoongkwon]

@marc_habermann Congrats🎉🎉

[MrNeRF @janusch_patas]

VRGaussianAvatar: Integrating 3D Gaussian Avatars into VR

TL;DR: VRGaussianAvatar is an integrated system that enables real-time full-body 3DGS avatars in virtual reality using only HMD tracking signals.

Contributions:
• VRGaussianAvatar: an integrated VR streaming system for stereoscopic rendering of a 3DGS full-body avatar driven by HMD-only tracking.

• Binocular Batching: an efficient stereoscopic rendering strategy that jointly renders the left and right eye views in a single batched pass for 3DGS avatars in VR.

• A quantitative evaluation and perceptual user study showing improved perceived appearance similarity, embodiment, and plausibility over mesh-based avatar baselines under the same HMD-only control setting.

[𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 @techwith_ram]

Linear Algebra for Computer Vision, Robotics, and Machine Learning from the University of Pennsylvania.

https://t.co/3PLbHINqwb

If you want to stop treating ML like magic and start understanding it as mathematics, this is the right starting point.

[Math Cafe @Riazi_Cafe_en]

Stanford's "Mathematical Methods for Computer Vision, Robotics, and Graphics"

PDF: https://t.co/75DVMuAp5B
Video Lectures: https://t.co/iQCqMmwZDl

[Zhenjun Zhao @zhenjun_zhao]

AnyView: Synthesizing Any Novel View in Dynamic Scenes

@basilevanh, @dian_chen_, Shun Iwase, @ptokmakov, @mzubairirshad, @vslevic, Swati Gupta, Fangzhou Cheng, @ZakharovSergeyN, Vitor Campagnolo Guizilini

tl;dr: in title; DiT-based

https://t.co/RxBYm0Acvw

[Haven Feng @HavenFeng]

✨Thinking with Blender~
Meet VIGA: a multimodal agent that autonomously codes 3D/4D blender scenes from any image, with no human, no training!
@berkeley_ai #LLMs #Blender #Agent 🧵1/6

[Vanessa Sklyarova @ness_pirs]

Excited to share our new work, NeuralFur 🐾🐻
The first method to reconstruct strand-based 3D animal fur from multi-view images, compatible with graphics & simulation pipelines.
Accepted as an oral at #3DV2026
🔗 https://t.co/mROCZ04l2E

[Chen Geng @gengchen01]

✨ Any static 3D assets ➡️ 4D dynamic worlds.
Introducing CHORD, a universal framework for generating scene-level 4D dynamic motion from any static 3D inputs.

It generalizes surprisingly well across a wide range of objects 🤯 and can even be used to learn robotics manipulation policy 🤖!

Project page: https://t.co/KOHlqgbvIp.

Dive deeper in a 🧵: 1/n