Abstract
Generative modelling over continuous-time geometric constructs, a.k.a chirographic data such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Such strictly-ordered discrete factorization however falls short of capturing key properties of chirographic data -- it fails to build holistic understanding of the temporal concept due to one-way visibility (causality). Consequently, temporal data has been modelled as discrete token sequences of fixed sampling rate instead of capturing the true underlying concept. In this paper, we introduce a powerful model-class namely Denoising\ Diffusion\ Probabilistic\ Models or DDPMs for chirographic data that specifically addresses these flaws. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate up to a good extent. Moreover, we show that many important downstream utilities (e.g. conditional sampling, creative mixing) can be flexibly implemented using ChiroDiff. We further show some unique use-cases like stochastic vectorization, de-noising/healing, abstraction are also possible with this model-class. We perform quantitative and qualitative evaluation of our framework on relevant datasets and found it to be better or on par with competing approaches.
Original language | English |
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Title of host publication | The Eleventh International Conference on Learning Representations |
Subtitle of host publication | ICLR 2023 |
Number of pages | 15 |
Publication status | Published - 1 May 2023 |
Event | The Eleventh International Conference on Learning Representations - Kigali, Rwanda Duration: 1 May 2023 → 5 May 2023 https://iclr.cc/Conferences/2023 |
Conference
Conference | The Eleventh International Conference on Learning Representations |
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Abbreviated title | ICLR 2023 |
Country/Territory | Rwanda |
City | Kigali |
Period | 1/05/23 → 5/05/23 |
Internet address |