Recent advances in text-to-image diffusion models have been driven by the increasing availability of paired 2D data. However, the development of 3D diffusion models has been hindered by the scarcity of high-quality 3D data, resulting in less competitive performance compared to their 2D counterparts. To address this challenge, we propose repurposing pre-trained 2D diffusion models for 3D object generation. We introduce Gaussian Atlas, a novel representation that utilizes dense 2D grids, enabling the fine-tuning of 2D diffusion models to generate 3D Gaussians. Our approach demonstrates successful transfer learning from a pre-trained 2D diffusion model to a 2D manifold flattened from 3D structures. To support model training, we compile GaussianVerse, a large-scale dataset comprising 205K high-quality 3D Gaussian fittings of various 3D objects. Our experimental results show that text-to-image diffusion models can be effectively adapted for 3D content generation, bridging the gap between 2D and 3D modeling.

To build photorealistic avatars that users can embody, human modelling must be complete (cover the full body), driveable (able to reproduce the current motion and appearance from the user), and generalizable (i.e., easily adaptable to novel identities).Towards these goals, paired captures, that is, captures of the same subject obtained from systems of diverse quality and availability, are crucial.However, paired captures are rarely available to researchers outside of dedicated industrial labs: Codec Avatar Studio is our proposal to close this gap.Towards generalization and driveability, we introduce a dataset of 256 subjects captured in two modalities: high resolution multi-view scans of their heads, and video from the internal cameras of a headset.Towards completeness, we introduce a dataset of 4 subjects captured in eight modalities: high quality relightable multi-view captures of heads and hands, full body multi-view captures with minimal and regular clothes, and corresponding head, hands and body phone captures.Together with our data, we also provide code and pre-trained models for different state-of-the-art human generation models.Our datasets and code are available at https://github.com/facebookresearch/ava-256 and https://github.com/facebookresearch/goliath.

In this paper, we propose an approach of clustering data in parallel coordinates through interactive local operations. Different from many other methods in which clustering is globally applied to the whole dataset, our interactive scheme allows users to directly apply attractive and repulsive operators at regions of interests, taking advantages of an electricity interaction metaphor, for clutter reduction and cluster detection. Our design enables users to interact directly with the parallel coordinate plots and provides great flexibility in exploring and revealing underlying patterns. With instant feedback, our work allows users to dynamically adjust the clustering parameters to reach an optimum. We also supply the user with a graph indicating the logical relationship between clusters. Our experiments show that our scheme is more efficient than traditional methods in performing visual analysis tasks.

Over the past few years, large human populations around the world have been affected by an increase in significant seismic activities. For both conducting basic scientific research and for setting critical government policies, it is crucial to be able to explore and understand seismic and geographical information obtained through all scientific instruments. In this work, we present a visual analytics system that enables explorative visualization of seismic data together with satellite-based observational data, and introduce a suite of visual analytical tools. Seismic and satellite data are integrated temporally and spatially. Users can select temporal ;and spatial ranges to zoom in on specific seismic events, as well as to inspect changes both during and after the events. Tools for designing high dimensional transfer functions have been developed to enable efficient and intuitive comprehension of the multi-modal data. Spread-sheet style comparisons are used for data drill-down as well as presentation. Comparisons between distinct seismic events are also provided for characterizing event-wise differences. Our system has been designed for scalability in terms of data size, complexity (i.e. number of modalities), and varying form factors of display environments.

In this paper, we present a novel parallel coordinates design integrated with points (Scattering Points in Parallel Coordinates, SPPC), by taking advantage of both parallel coordinates and scatterplots. Different from most multiple views visualization frameworks involving parallel coordinates where each visualization type occupies an individual window, we convert two selected neighboring coordinate axes into a scatterplot directly. Multidimensional scaling is adopted to allow converting multiple axes into a single subplot. The transition between two visual types is designed in a seamless way. In our work, a series of interaction tools has been developed. Uniform brushing functionality is implemented to allow the user to perform data selection on both points and parallel coordinate polylines without explicitly switching tools. A GPU accelerated Dimensional Incremental Multidimensional Scaling (DIMDS) has been developed to significantly improve the system performance. Our case study shows that our scheme is more efficient than traditional multi-view methods in performing visual analysis tasks.