Publications - Current Year

We introduce MEt3R, a metric for multi-view consistency in generated images.
Large-scale generative models for multi-view image generation are rapidly
advancing the field of 3D inference from sparse observations. However, due to
the nature of generative modeling, traditional reconstruction metrics are not
suitable to measure the quality of generated outputs and metrics that are
independent of the sampling procedure are desperately needed. In this work, we
specifically address the aspect of consistency between generated multi-view
images, which can be evaluated independently of the specific scene. Our
approach uses DUSt3R to obtain dense 3D reconstructions from image pairs in a
feed-forward manner, which are used to warp image contents from one view into
the other. Then, feature maps of these images are compared to obtain a
similarity score that is invariant to view-dependent effects. Using MEt3R, we
evaluate the consistency of a large set of previous methods for novel view and
video generation, including our open, multi-view latent diffusion model.

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