3 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
Visual Jenga: Discovering Object Dependencies via Counterfactual Inpainting
Anand Bhattad, Konpat Preechakul, Alexei A. Efros
This paper proposes a novel scene understanding task called Visual Jenga. Drawing inspiration from the game Jenga, the proposed task involves progressively removing objects from a…
Optimizing Diffusion Noise Can Serve As Universal Motion Priors
Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan +3
We propose Diffusion Noise Optimization (DNO), a new method that effectively leverages existing motion diffusion models as motion priors for a wide range of motion-related tasks. I…
Guided Motion Diffusion for Controllable Human Motion Synthesis
Korrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn +1
Denoising diffusion models have shown great promise in human motion synthesis conditioned on natural language descriptions. However, integrating spatial constraints, such as pre-de…
Diffusion Autoencoders: Toward a Meaningful and Decodable Representation
Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa +1
Diffusion probabilistic models (DPMs) have achieved remarkable quality in image generation that rivals GANs'. But unlike GANs, DPMs use a set of latent variables that lack semantic…
High resolution weakly supervised localization architectures for medical images
Konpat Preechakul, Sira Sriswasdi, Boonserm Kijsirikul +1
In medical imaging, Class-Activation Map (CAM) serves as the main explainability tool by pointing to the region of interest. Since the localization accuracy from CAM is constrained…