Publications - Last Year

Markov chain Monte Carlo (MCMC) algorithms come to rescue when sampling from
a complex, high-dimensional distribution by a conventional method is
intractable. Even though MCMC is a powerful tool, it is also hard to control
and tune in practice. Simultaneously achieving both local exploration of the
state space and global discovery of the target distribution is a challenging
task. In this work, we present a MCMC formulation that subsumes all existing
MCMC samplers employed in rendering. We then present a novel framework for
adjusting an arbitrary Markov chain, making it exhibit invariance with respect
to a specified target distribution. To showcase the potential of the proposed
framework, we focus on a first simple application in light transport
simulation. As a by-product, we introduce continuous-time MCMC sampling to the
computer graphics community. We show how any existing MCMC-based light
transport algorithm can be embedded into our framework. We empirically and
theoretically prove that this embedding is superior to running the standalone
algorithm. In fact, our approach will convert any existing algorithm into a
highly parallelizable variant with shorter running time, smaller error and less
variance.

Classical generative diffusion models learn an isotropic Gaussian denoising
process, treating all spatial regions uniformly, thus neglecting potentially
valuable structural information in the data. Inspired by the long-established
work on anisotropic diffusion in image processing, we present a novel
edge-preserving diffusion model that is a generalization of denoising diffusion
probablistic models (DDPM). In particular, we introduce an edge-aware noise
scheduler that varies between edge-preserving and isotropic Gaussian noise. We
show that our model's generative process converges faster to results that more
closely match the target distribution. We demonstrate its capability to better
learn the low-to-mid frequencies within the dataset, which plays a crucial role
in representing shapes and structural information. Our edge-preserving
diffusion process consistently outperforms state-of-the-art baselines in
unconditional image generation. It is also more robust for generative tasks
guided by a shape-based prior, such as stroke-to-image generation. We present
qualitative and quantitative results showing consistent improvements (FID
score) of up to 30% for both tasks. We provide source code and supplementary
content via the public domain edge-preserving-diffusion.mpi-inf.mpg.de .

In this paper, we propose a novel encoder-decoder architecture, named SABER,
to learn the 6D pose of the object in the embedding space by learning shape
representation at a given pose. This model enables us to learn pose by
performing shape representation at a target pose from RGB image input. We
perform shape representation as an auxiliary task which helps us in learning
rotations space for an object based on 2D images. An image encoder predicts the
rotation in the embedding space and the DeepSDF based decoder learns to
represent the object's shape at the given pose. As our approach is shape based,
the pipeline is suitable for any type of object irrespective of the symmetry.
Moreover, we need only a CAD model of the objects to train SABER. Our pipeline
is synthetic data based and can also handle symmetric objects without symmetry
labels and, thus, no additional labeled training data is needed. The
experimental evaluation shows that our method achieves close to benchmark
results for both symmetric objects and asymmetric objects on Occlusion-LineMOD,
and T-LESS datasets.

Accurate property prediction is crucial for accelerating the discovery of new
molecules. Although deep learning models have achieved remarkable success,
their performance often relies on large amounts of labeled data that are
expensive and time-consuming to obtain. Thus, there is a growing need for
models that can perform well with limited experimentally-validated data. In
this work, we introduce MoleVers, a versatile pretrained model designed for
various types of molecular property prediction in the wild, i.e., where
experimentally-validated molecular property labels are scarce. MoleVers adopts
a two-stage pretraining strategy. In the first stage, the model learns
molecular representations from large unlabeled datasets via masked atom
prediction and dynamic denoising, a novel task enabled by a new branching
encoder architecture. In the second stage, MoleVers is further pretrained using
auxiliary labels obtained with inexpensive computational methods, enabling
supervised learning without the need for costly experimental data. This
two-stage framework allows MoleVers to learn representations that generalize
effectively across various downstream datasets. We evaluate MoleVers on a new
benchmark comprising 22 molecular datasets with diverse types of properties,
the majority of which contain 50 or fewer training labels reflecting real-world
conditions. MoleVers achieves state-of-the-art results on 20 out of the 22
datasets, and ranks second among the remaining two, highlighting its ability to
bridge the gap between data-hungry models and real-world conditions where
practically-useful labels are scarce.