Research Projects and Publications

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TrajectoryMover: Generative Movement of Object Trajectories in Videos


Kiran Chhatre, Hyeonho Jeong, Yulia Gryaditskaya, Christopher E. Peters, Chun-Hao Paul Huang, Paul Guerrero
arXiv preprint, 2026
arxiv / website / code /

A scene-aware video editing method that moves an object to a new starting position while generating a plausible new trajectory and preserving the surrounding scene.

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


Rajmund Nagy, Hendric Voß, Thanh Hoang-Minh, Mihail Tsakov, Teodor Nikolov, Zeyi Zhang, Tenglong Ao, Sicheng Yang, Shaoli Huang, Yongkang Cheng, M. Hamza Mughal, Rishabh Dabral, Kiran Chhatre, Christian Theobalt, Libin Liu, Stefan Kopp, Rachel McDonnell, Michael Neff, Taras Kucherenko, Youngwoo Yoon, Gustav Eje Henter
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings Track, 2026
arxiv / website / video / code /

We introduce a standard user-study protocol for evaluating speech-driven 3D gesture models and benchmark six methods on BEAT2. We release videos, code, and human ratings to support fair comparison.

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Spectrum: Learning 3D Texture-Aware Representations for Parsing Diverse Human Clothing and Body Parts


Kiran Chhatre, Christopher Peters, Srikrishna Karanam
Association for the Advancement of Artificial Intelligence (AAAI), 2026
arxiv / website / poster / x thread / patent /

A 3D texture-aware diffusion representation for open-vocabulary parsing of clothing and body parts across diverse poses, outfits, and multi-person scenes.

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Synthetically Expressive: Evaluating gesture and voice for emotion and empathy in VR and 2D scenariosbest paper award


Haoyang Du, Kiran Chhatre, Christopher Peters, Brian Keegan, Rachel McDonnell, Cathy Ennis
ACM International Conference on Intelligent Virtual Agents (IVA), 2025
arxiv / website / youtube /

This work evaluates gesture and voice synthesis for conveying emotion and empathy in both VR and 2D scenarios, providing insights into the effectiveness of synthetic emotional expressions across different interaction modalities.

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Evaluation of Generative Models for Emotional 3D Animation Generation in VR


Kiran Chhatre, Renan Guarese, Andrii Matviienko, Christopher Peters
Frontiers in Computer Science (Human-Media Interaction) & ACM SIGGRAPH I3D, 2025
arxiv / i3d workshop / website / video / supp. material /

This work evaluates emotional 3D animation generative models within an immersive Virtual Reality environment, emphasizing user-centric metrics including emotional arousal realism, naturalness, enjoyment, diversity, face-body congruence, and interaction quality in real-time human-agent interaction scenarios.

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Audiopedia: Audio QA with Knowledgeoral


Abhirama S. Penamakuri*, Kiran Chhatre*, Akshat Jain (* denotes equal contribution)
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025
arxiv / website / code /

A knowledge-intensive audio question-answering benchmark and method that improves audio-language models using external knowledge.

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AMUSE: Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion


Kiran Chhatre, Radek Daněček, Nikos Athanasiou, Giorgio Becherini, Christopher Peters, Michael J. Black, Timo Bolkart
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
arxiv / website / youtube / code / poster / x thread /

A latent-diffusion model for generating controllable emotional 3D body motion directly from speech.

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EMOTE: Emotional Speech-Driven Animation with Content-Emotion Disentanglement


Radek Daněček, Kiran Chhatre, Shashank Tripathi, Yandong Wen, Michael J. Black, Timo Bolkart
ACM SIGGRAPH Asia Conference Papers, 2023
arxiv / website / video / code / x thread /

A speech-driven 3D facial animation method that generates synchronized and emotionally expressive facial motion from audio and an emotion label.

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BEAMBayesOpt: Parallel Bayesian Optimization of Agent-Based Transportation Simulationspecial mentions


Kiran Chhatre, Sidney Feygin, Colin Sheppard, Rashid Waraich
Springer Nature International Conference on Machine Learning, Optimization, and Data Science (LOD), 2022
paper / code / BEAM-integration /

BEAMBayesOpt introduces a parallel Bayesian optimization approach with early stopping that autonomously calibrates hyperparameters in BEAM’s large-scale multi-agent transportation simulations and enables efficient surrogate modeling of complex scenarios.

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Spatio-temporal priors in 3D human motion


Anna Deichler*, Kiran Chhatre*, Christopher Peters, Jonas Beskow (* denotes equal contribution)
IEEE International Conference on Development and Learning (StEPP) workshop, 2021
arxiv / website /

This workshop paper investigates spatial-temporal priors for 3D human motion synthesis by comparing graph convolutional networks and transformer architectures to capture dynamic joint dependencies.

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Rethinking Computer-Aided Architectural Design (CAAD) – From Generative Algorithms and Architectural Intelligence to Environmental Design and Ambient Intelligence


Todor Stojanovski, Hui Zhang, Emma Frid, Kiran Chhatre, Christopher Peters, Ivor Samuels, Paul Sanders, Jenni Partanen, Deborah Lefosse
Springer Nature International Conference on Computer-Aided Architectural Design Futures (CAAD Futures), 2021
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This paper reviews the evolution of CAAD—from generative algorithms and BIM to current AI developments—and argues that integrating AI-driven ambient intelligence into digital design tools can transform architectural and urban design for smarter, more sustainable cities.


Updated on: 2026-07-12


Thanks, Jon Barron!