most citedMoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

5 citations · 5 across the 2 of their papers we have counts for

collaborators

18 papers

cs.CV2026

Towards Real-World Wearable Motion Reconstruction

Andrea Boscolo Camiletto, Rishabh Dabral, Eduardo Alvarado +3

The modern-day surge in popularity of wearable devices poses a fundamentally unique motion capture problem: reconstructing full-body movement from any set of sensing hardware worn…

cs.CV20265 cited

MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

Rishabh Dabral, Muhammad Hamza Mughal, Vladislav Golyanik +1

Conventional methods for human motion synthesis are either deterministic or struggle with the trade-off between motion diversity and motion quality. In response to these limitation…

cs.CV2026

Towards Reliable Human Evaluations in Gesture Generation: Insights from a Community-Driven State-of-the-Art Benchmark

Rajmund Nagy, Hendric Voss, Thanh Hoang-Minh +18

We review human evaluation practices in automatic, speech-driven 3D gesture generation and find a lack of standardisation and frequent use of flawed experimental setups. This leads…

cs.CV2026

VHOI: Controllable Video Generation of Human-Object Interactions from Sparse Trajectories via Motion Densification

Wanyue Zhang, Lin Geng Foo, Thabo Beeler +2

Synthesizing realistic human-object interactions (HOI) in video is challenging due to the complex, instance-specific interaction dynamics of both humans and objects. Incorporating…

cs.CV2026

Relightable Holoported Characters: Capturing and Relighting Dynamic Human Performance from Sparse Views

Kunwar Maheep Singh, Jianchun Chen, Vladislav Golyanik +5

We present Relightable Holoported Characters (RHC), a novel person-specific method for free-view rendering and relighting of full-body and highly dynamic humans solely observed fro…

cs.CV2026

SceMoS: Scene-Aware 3D Human Motion Synthesis by Planning with Geometry-Grounded Tokens

Anindita Ghosh, Vladislav Golyanik, Taku Komura +3

Synthesizing text-driven 3D human motion within realistic scenes requires learning both semantic intent ("walk to the couch") and physical feasibility (e.g., avoiding collisions).…