Jan Eric Lenssen (Senior Researcher)

We introduce PersonaHOI, a training- and tuning-free framework that fuses a
general StableDiffusion model with a personalized face diffusion (PFD) model to
generate identity-consistent human-object interaction (HOI) images. While
existing PFD models have advanced significantly, they often overemphasize
facial features at the expense of full-body coherence, PersonaHOI introduces an
additional StableDiffusion (SD) branch guided by HOI-oriented text inputs. By
incorporating cross-attention constraints in the PFD branch and spatial merging
at both latent and residual levels, PersonaHOI preserves personalized facial
details while ensuring interactive non-facial regions. Experiments, validated
by a novel interaction alignment metric, demonstrate the superior realism and
scalability of PersonaHOI, establishing a new standard for practical
personalized face with HOI generation. Our code will be available at
github.com/JoyHuYY1412/PersonaHOI

We introduce Spatial Reasoning Models (SRMs), a framework to perform
reasoning over sets of continuous variables via denoising generative models.
SRMs infer continuous representations on a set of unobserved variables, given
observations on observed variables. Current generative models on spatial
domains, such as diffusion and flow matching models, often collapse to
hallucination in case of complex distributions. To measure this, we introduce a
set of benchmark tasks that test the quality of complex reasoning in generative
models and can quantify hallucination. The SRM framework allows to report key
findings about importance of sequentialization in generation, the associated
order, as well as the sampling strategies during training. It demonstrates, for
the first time, that order of generation can successfully be predicted by the
denoising network itself. Using these findings, we can increase the accuracy of
specific reasoning tasks from 1% to >50%.

We introduce SLayR, Scene Layout Generation with Rectified flow.
State-of-the-art text-to-image models achieve impressive results. However, they
generate images end-to-end, exposing no fine-grained control over the process.
SLayR presents a novel transformer-based rectified flow model for layout
generation over a token space that can be decoded into bounding boxes and
corresponding labels, which can then be transformed into images using existing
models. We show that established metrics for generated images are inconclusive
for evaluating their underlying scene layout, and introduce a new benchmark
suite, including a carefully designed repeatable human-evaluation procedure
that assesses the plausibility and variety of generated layouts. In contrast to
previous works, which perform well in either high variety or plausibility, we
show that our approach performs well on both of these axes at the same time. It
is also at least 5x times smaller in the number of parameters and 37% faster
than the baselines. Our complete text-to-image pipeline demonstrates the added
benefits of an interpretable and editable intermediate representation.

Much of the world's most valued data is stored in relational databases and
data warehouses, where the data is organized into many tables connected by
primary-foreign key relations. However, building machine learning models using
this data is both challenging and time consuming. The core problem is that no
machine learning method is capable of learning on multiple tables
interconnected by primary-foreign key relations. Current methods can only learn
from a single table, so the data must first be manually joined and aggregated
into a single training table, the process known as feature engineering. Feature
engineering is slow, error prone and leads to suboptimal models. Here we
introduce an end-to-end deep representation learning approach to directly learn
on data laid out across multiple tables. We name our approach Relational Deep
Learning (RDL). The core idea is to view relational databases as a temporal,
heterogeneous graph, with a node for each row in each table, and edges
specified by primary-foreign key links. Message Passing Graph Neural Networks
can then automatically learn across the graph to extract representations that
leverage all input data, without any manual feature engineering. Relational
Deep Learning leads to more accurate models that can be built much faster. To
facilitate research in this area, we develop RelBench, a set of benchmark
datasets and an implementation of Relational Deep Learning. The data covers a
wide spectrum, from discussions on Stack Exchange to book reviews on the Amazon
Product Catalog. Overall, we define a new research area that generalizes graph
machine learning and broadens its applicability to a wide set of AI use cases.

We present TOCH, a method for refining incorrect 3D hand-object interaction
sequences using a data prior. Existing hand trackers, especially those that
rely on very few cameras, often produce visually unrealistic results with
hand-object intersection or missing contacts. Although correcting such errors
requires reasoning about temporal aspects of interaction, most previous work
focus on static grasps and contacts. The core of our method are TOCH fields, a
novel spatio-temporal representation for modeling correspondences between hands
and objects during interaction. The key component is a point-wise
object-centric representation which encodes the hand position relative to the
object. Leveraging this novel representation, we learn a latent manifold of
plausible TOCH fields with a temporal denoising auto-encoder. Experiments
demonstrate that TOCH outperforms state-of-the-art (SOTA) 3D hand-object
interaction models, which are limited to static grasps and contacts. More
importantly, our method produces smooth interactions even before and after
contact. Using a single trained TOCH model, we quantitatively and qualitatively
demonstrate its usefulness for 1) correcting erroneous reconstruction results
from off-the-shelf RGB/RGB-D hand-object reconstruction methods, 2) de-noising,
and 3) grasp transfer across objects. We will release our code and trained
model on our project page at virtualhumans.mpi-inf.mpg.de/toch/