Praneet C. Bala

Praneet C. Bala

I build systems that see, reconstruct, and reason about the 3D world.

Resume

I’m a Ph.D. candidate in Computer Science at the University of Minnesota, Twin Cities, advised by Dr. Hyun Soo Park and Dr. Jan Zimmermann. My research focuses on 3D Computer Vision, self-supervised learning, and generative modeling, building systems that perceive, reconstruct, and predict the physical world from visual data.

My work spans the full stack: multi-camera capture systems, contrastive representation learning, large-scale dataset creation, latent diffusion models, and neural rendering. I have 4 publications in Nature Communications, IJCV, and eLife, and a paper on domain-aware 3D motion forecasting under review at ECCV 2026.

My research maps directly onto problems in autonomous vehicles, embodied AI, and generative 3D.

Projects

Learning to Forecast Domain-Aware 3D Body Motion
Self-supervised framework for predicting 3D body motion from monocular video without 3D annotations. Combines spatiotemporal transformers with latent diffusion for realistic long-term motion forecasting.
PyTorch Self-supervised Diffusion ECCV 2026
OpenMonkeyStudio
62-camera markerless motion capture system for high-fidelity 3D body tracking in unconstrained environments. Released public dataset of 195,228 annotated pose frames.
TensorFlow Multi-view 3D Reconstruction Nature Communications 2020
Paper →
Self-supervised Secondary Landmark Detection
Self-supervised method using 3D representation learning and contrastive learning to detect anatomically consistent landmarks without manual annotation. Validated across macaques, humans, and flies.
TensorFlow Contrastive Learning IJCV 2023
CLIP-Guided Pose-Conditioned Image Generation
Fine-tuned Stable Diffusion via ControlNet on 80,000 pose images using CLIP text conditioning and 2D pose control signals.
PyTorch ControlNet Stable Diffusion Generative AI
3D Scene Reconstruction via Gaussian Splatting
Video-to-3D reconstruction pipeline using COLMAP and Gaussian Splatting for interactive novel view synthesis and scene-level optimization.
COLMAP Gaussian Splatting Neural Rendering

Publications

Bala et al., “Learning to Forecast Domain Aware 3D Body Motion by Watching Single View Videos.” European Conference on Computer Vision, 2026. Under Review

News

Service

Reviewing
Invited Talks
Automated Markerless Pose Estimation in Freely Moving Macaques with OpenMonkeyStudio
UMN Center for Neuroengineering seminar series
UMN Visual Computing & AI seminar series
Teaching