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@niksteel123 @ayushjain1144 We haven’t considered using game engines for capturing scene + temporal data - we are mostly using open source 3D benchmarks and simulators. We do have a follow up project on the 4D scenes, @ayushjain1144 is leading this
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🚀Excited to share Qwen-3D (ECCV 2026) — a generalist 3D vision–language model that does grounding, segmentation, VQA, and spatial reasoning in one place. VLMs are great at images and short clips, but long videos bury them in tokens. Our idea: put multi-view frames into a shared 3D world, so attention scales with what’s in the scene, not how many frames you feed it. 📄arxiv.org/abs/2608.02980 🌐qwen-3d.github.io
Paper / code / checkpoints are all up: 📄 arxiv.org/abs/2608.02980 🌐 qwen-3d.github.io 💻 github.com/ll220/qwen3d 🤗 huggingface.co/collections/ka… w/ @ayushjain1144†, Yifan Liu, and @KaterinaFragiad · CMU † equal contribution
🔬 We find that our gains come from: Full text+visual token interface, 3D RoPE, full attention over visual tokens, and LoRA finetuning of the VLM.
So why doesn’t everyone just do this? The data just isn't there. We’ve got ~1B images… and maybe ~10k posed 3D scenes. Pure 3D-first models don’t scale today. So Qwen-3D is a unified 2D–3D model: shared parameters, shared token space, trained with both. Keep the scale of 2D data; add geometry where we have it.
We may receive 2D frames from sensors, but the world is inherently 3D - many of the frames we receive are redundant due to overlapping views. Once you lift RGB-D into world space, those redundant views collapse into persistent scene tokens. Same room, 400 frames or 75 — similar token budget. Having a model that runs on these kinds of inputs wins you 📦Compactness · 🗺️Spatial reasoning · ⏱️Temporal consistency
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