Articles by Dmytro Spodarets
159 items
Neural Preset for Color Style Transfer
Neural Preset is a technique that uses AI to generate and transfer color styles. It can extract color styles from given reference images, store them as presets, and apply them to other images and videos, producing output with target color styles. Check it out!
May 12, 2023
BloombergGPT: A Large Language Model for Finance
BloombergGPT is a 50 billion parameter language model that is trained on a wide range of financial data. It is validated on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that most accurately reflect our intended usage.
Apr 25, 2023
S-NeRF: Neural Radiance Fields for Street Views
In this paper, the authors propose a new street-view NeRF (S-NeRF) that considers novel view synthesis of both the large-scale background scenes and the foreground moving vehicles jointly. Learn more about their approach and the results of experiments!
Mar 27, 2023
Universal Guidance for Diffusion Models
Typical diffusion models cannot be conditioned on other modalities without retraining. This work presents a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components.
Mar 17, 2023
3D Generation on ImageNet
In this paper, the authors develop a 3D generator with Generic Priors (3DGP): a 3D synthesis framework with more general assumptions about the training data, and show that it scales to challenging datasets, like ImageNet. It is based on three new ideas. Learn them!
Mar 7, 2023
3D-aware Conditional Image Synthesis
This paper describes a 3D-aware conditional generative model for controllable photorealistic image synthesis. It integrates 3D representations with conditional generative modeling, i.e., enabling controllable high-resolution 3D-aware rendering by conditioning on user inputs.
Mar 5, 2023
SinMDM: Single Motion Diffusion
This paper presents a Single Motion Diffusion Model, dubbed SinMDM, a model designed to learn the internal motifs of a single motion sequence with arbitrary topology and synthesize motions of arbitrary length that are faithful to them. Check it out!
Mar 2, 2023
Data Phoenix Digest - ISSUE 5.2023
Special event "Startup’s Guide to Success in Central and Eastern Europe" at NVIDIA GTC, scaling media ML at Netflix, how to transform time series for DL, time series forecasting with DL in PyTorch, Megane, OmniObject3D, LEGO-Net, Offsite-Tuning, news, and more.
Mar 1, 2023
LEGO-Net: Learning Regular Rearrangements of Objects in Rooms
LEGO-Net is a data-driven transformer-based iterative method for LEarning reGular rearrangement of Objects in messy rooms. Results demonstrate that the method is able to reliably rearrange room scenes and outperform other methods. Find out more!
Feb 23, 2023
OmniObject3D
OmniObject3D is a large vocabulary 3D object dataset featuring massive high-quality real-scanned 3D objects that can be used to facilitate the development of 3D perception, reconstruction, and generation in the real world. Give it a try!
Feb 19, 2023
MEGANE: Morphable Eyeglass and Avatar Network
Megane is a 3D compositional morphable model of eyeglasses that incorporates high-fidelity geometric and photometric interaction effects. To support the variation in eyeglass topology, a hybrid representation of surface geometry and a volumetric representation is employed.
Feb 14, 2023
Data Phoenix Digest - ISSUE 4.2023
Video recording of our webinar about deploying ML models, guide to the YOLO family, discover the 4 magical methods to detect AI-generated text, BentoML vs. FastAPI, Temporal Graph Learning, exploring MLflow experiments, watermark for Large Language Models, EPiC-GAN, Tracr, Msanii, news, and more.
Feb 14, 2023