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Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their... applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.show more

AK

516,258 subscribers

149,079 Aufrufe • vor 2 Jahren •via X (Twitter)

Wissenschaft & Technologie

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10 Kommentare

Profilbild von AK

AKvor 2 Jahren

discuss:

Profilbild von A.I.Warper

A.I.Warpervor 2 Jahren

Ali “no code, code coming soon, jk it’s never coming” baba

Profilbild von Kamus

Kamusvor 2 Jahren

these are great, but i wish everytime i read on one of these, i could try it out immediately.

Profilbild von Aswanth achoo'z

Aswanth achoo'zvor 2 Jahren

Rip motion tracking 😯

Profilbild von Curt Anderson

Curt Andersonvor 2 Jahren

Isn't this very similar to what ControlNet does? ControlNet also is able to make accurate wireframe interpretations of poses, but this does seem more coherent.

Profilbild von Tomy Kwong 𝕏

Tomy Kwong 𝕏vor 2 Jahren

MIMO as in Wi-Fi signalling? /s

Profilbild von txh

txhvor 2 Jahren

open-source?

Profilbild von Fareesh Vijayarangam

Fareesh Vijayarangamvor 2 Jahren

new fone who dis

Profilbild von Diallo Ciré

Diallo Cirévor 2 Jahren

When I say I want to do research in CV this might be the reason

Profilbild von ari

arivor 2 Jahren

I'd be cautious as these Alibaba papers will never release the code and also rarely publish working demo or product. Just impressive paper videos

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58,572 Aufrufe • vor 2 Jahren

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Zhiyang (Frank) Dou

14,728 Aufrufe • vor 1 Jahr

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Chuang Gan

43,265 Aufrufe • vor 1 Jahr

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Introducing Character Motion Control for Video Diffusion Models Creators currently lack control over how characters move and act in the videos they generate with AI. Today, we’re changing that. With Character Motion Control, you can act it out and bring your exact vision to life. How it works: 1️⃣ Describe the scene with a prompt 2️⃣ Upload an acting video to guide character movement 🎥 Generate a video where character motion is driven by the acting video. Register for Early Access via the link in the comments.

Kinetix

98,069 Aufrufe • vor 1 Jahr

animators are not needed anymore this 3D AI motion capture plugin can convert character movement from real video to 3D data and.. you can apply the motion to any 3D character.. link in comments

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el.cine

64,848 Aufrufe • vor 11 Monaten

🍺 LagerNVS (CVPR 2026) 🍺 LagerNVS is a generalizable, feed-forward, real-time Novel View Synthesis network which - performs rendering in real time, - generalizes to in-the-wild data, - works with and without known source cameras, - sets a new state-of-the-art among deterministic methods, - can be paired with a diffusion decoder for generative extrapolation. LagerNVS shows that 3D biases are useful for Novel View Synthesis but explicit 3D representations are not required to achieve them. We use 3D biases in (1) architecture design and (2) pre-training: (1) In NVS with explicit 3D representations (3DGS, NeRF) reconstruction is typically difficult and slow, but rendering is much faster and simpler. We mimic this process in the network design: we use a large (1B params) encoder and a small, lightweight decoder (ViT-B). This allows increasing the network capacity while still achieving real-time rendering. (2) The encoder, initialized from VGGT, was pre-trained with 3D reconstruction objectives, making the initial features 3D aware. Both substantially improve performance. Project page: Code: Paper: Models: Work done with Jianyuan Minghao Chen Christian Rupprecht and Andrea Vedaldi

🍺 LagerNVS (CVPR 2026) 🍺 LagerNVS is a generalizable, feed-forward, real-time Novel View Synthesis network which - performs rendering in real time, - generalizes to in-the-wild data, - works with and without known source cameras, - sets a new state-of-the-art among deterministic methods, - can be paired with a diffusion decoder for generative extrapolation. LagerNVS shows that 3D biases are useful for Novel View Synthesis but explicit 3D representations are not required to achieve them. We use 3D biases in (1) architecture design and (2) pre-training: (1) In NVS with explicit 3D representations (3DGS, NeRF) reconstruction is typically difficult and slow, but rendering is much faster and simpler. We mimic this process in the network design: we use a large (1B params) encoder and a small, lightweight decoder (ViT-B). This allows increasing the network capacity while still achieving real-time rendering. (2) The encoder, initialized from VGGT, was pre-trained with 3D reconstruction objectives, making the initial features 3D aware. Both substantially improve performance. Project page: Code: Paper: Models: Work done with Jianyuan Minghao Chen Christian Rupprecht and Andrea Vedaldi

Stan Szymanowicz

33,180 Aufrufe • vor 7 Monaten