Sai Kumar Dwivedi

Sai Kumar Dwivedi

@saidwivedi.in

PhD Candidate at @MPI-IS || 3D Vision & Digital Avatars || Ex: @Meta, @Daimler, @Intel Webpage: https://saidwivedi.in

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Why does 3D human-object reconstruction fail in the wild or get limited to a few object classes? A key missing piece is accurate 3D contact. InteractVLM (#CVPR2025) uses foundational models to infer contact on humans & objects, improving reconstruction from a single image. (1/10)

I use Claude Code in one terminal, Codex in another, and a training job beside them. Next month, I’ll use something else. I realized I don’t want remote access to an agent. I want remote access to where they all already live: my terminal. So I built PocketTUI.com [1/7]

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I use Claude Code in one terminal, Codex in another, and a training job beside them. Next month, I’ll use something else. I realized I don’t want remote access to an agent. I want remote access to where they all already live: my terminal. So I built PocketTUI.com [1/7]

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I wanted this to be comfortable to use from my phone. The terminal keys I need are built in: Esc, Ctrl, Tab, arrows. For longer instructions, I can just speak. Transcription runs on my machine and uses terminal context, so it gets filenames, flags and commands right. [4/7]

Everything keeps running in tmux on my machine. I can close the tab, lose the connection, or switch devices. When I come back, the same sessions are exactly where I left them. [5/7]

I use Claude Code in one terminal, Codex in another, and a training job beside them. Next month, I’ll use something else. I realized I don’t want remote access to an agent. I want remote access to where they all already live: my terminal. So I built PocketTUI.com [1/7]

PocketTUI connects to the terminal itself, so there’s nothing special to integrate. Claude Code, Codex, scripts, servers, builds, training jobs. They all stay in their real tmux sessions on your machine. If it runs in your terminal, it works with PocketTUI. [2/7]

But the terminal alone doesn’t cover everything I need. Sometimes I want to browse files, check what an agent changed, read Markdown, or open an image or video a job just produced. So I built the UI I needed around the terminal, in the same browser tab. [3/7]

Why does 3D human-object reconstruction fail in the wild or get limited to a few object classes? A key missing piece is accurate 3D contact. InteractVLM (#CVPR2025) uses foundational models to infer contact on humans & objects, improving reconstruction from a single image. (1/10)

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InteractVLM is the first method that infers 3D contacts on both humans and objects from in-the-wild images, and exploits these for 3D reconstruction via an optimization pipeline. In contrast, existing methods like PHOSA rely on handcrafted or heuristic-based contacts. (9/10)

Why does 3D human-object reconstruction fail in the wild or get limited to a few object classes? A key missing piece is accurate 3D contact. InteractVLM (#CVPR2025) uses foundational models to infer contact on humans & objects, improving reconstruction from a single image. (1/10)

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With just 5% of DAMON’s 3D body contact annotations, InteractVLM surpasses the fully-supervised DECO baseline trained on 100% of 3D annotations. This is promising for minimizing reliance on costly 3D data by using foundational models. (8/10)

Why does 3D human-object reconstruction fail in the wild or get limited to a few object classes? A key missing piece is accurate 3D contact. InteractVLM (#CVPR2025) uses foundational models to infer contact on humans & objects, improving reconstruction from a single image. (1/10)

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To bridge this 2D-to-3D gap, we propose "Render-Localize-Lift": - Render: 3D human/object meshes into multiview 2D images. - Localize: A Multiview Localization (MV-Loc) model, guided by VLM tokens, predicts 2D contact masks. - Lift: 2D contact masks to 3D. (5/10)

InteractVLM significantly outperforms prior work, both qualitatively and quantitatively, on in-the-wild 3D human (binary & semantic) contact prediction on the DAMON dataset. (6/10)

Why does 3D human-object reconstruction fail in the wild or get limited to a few object classes? A key missing piece is accurate 3D contact. InteractVLM (#CVPR2025) uses foundational models to infer contact on humans & objects, improving reconstruction from a single image. (1/10)

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Furthermore, simple binary contact (touching “any” object) misses the rich semantics of real multi-object interactions. Thus, we introduce a novel task - "Semantic Human Contact" estimation: predicting contact points on a human related to a specified object. (3/10)

How can we infer 3D contact with limited 3D data? InteractVLM exploits foundational models—a VLM & localization model fine tuned to reason about contact. Given an image & prompt, the VLM outputs tokens for localization. But these models work in 2D, while contact is 3D. (4/10)

Why does 3D human-object reconstruction fail in the wild or get limited to a few object classes? A key missing piece is accurate 3D contact. InteractVLM (#CVPR2025) uses foundational models to infer contact on humans & objects, improving reconstruction from a single image. (1/10)

Precisely inferring where humans contact objects from an image is hard due to occlusion & depth ambiguity. Current datasets of images with 3D contact are small as they’re costly & tedious to create (mocap/manual labeling), limiting performance of contact predictors. (2/10)

✨ Happy to be recognised again as an Outstanding Reviewer for #CVPR2025!

#CVPR2026@cvprconference.bsky.social

Behind every great conference is a team of dedicated reviewers. Congratulations to this year’s #CVPR2025 Outstanding Reviewers! cvpr.thecvf.com/Conferences/...

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