Data Phoenix Digest - ISSUE 2.2023 Paid Members Public
Video recording of our webinar about dstack and reproducible ML workflows, AVL binary tree operations, Ultralytics YOLOv8, training XGBoost, productionize ML models, introduction to forecasting ensembles, domain expansion of image generators, Muse, X-Decoder, Box2Mask, RoDynRF, AgileAvatar and more.
AVL Binary Tree Operations Paid Members Public
In this article, the author described AVL trees and operations you can perform on them, such as inserting a node in different variants (for example, left, right or right, right).
RoDynRF: Robust Dynamic Radiance Fields Paid Members Public
In this work, the authors address the robustness issue of dynamic radiance field reconstruction methods by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). Learn how they do it!
AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging Paid Members Public
AgileAvatar is a novel self-supervised learning framework to create high-quality stylized 3D avatars with a mix of continuous and discrete parameters. To ensure the discrete parameters are optimized, a cascaded relaxation-and-search pipeline is implemented.
Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution Paid Members Public
Box2Mask is a novel single-shot instance segmentation approach, which integrates the classical level-set evolution model into deep neural network learning to achieve accurate mask prediction with only bounding box supervision. Check the paper out!
Zero-Shot Text-Guided Object Generation with Dream Fields Paid Members Public
Dream Fields can generate the geometry and color of a wide range of objects without 3D supervision. It combines neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Take a look!
Data Phoenix Digest - ISSUE 1.2023 Paid Members Public
Building a GitOps ML model registry with DVC and GTO, 10 metrics to evaluate supervised ML models, scaling ML model development with MLflow, ChatGPT and DALL·E 2 in a panel app, scalable diffusion models with transformers, NeRF-Art, ECON, InstantAvatar, DifFace, TextBox 2.0, ClimateNeRF, and more.
InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds Paid Members Public
InstantAvatar is a system that can reconstruct human avatars from a monocular video within seconds, and these avatars can be animated and rendered at an interactive rate. It converges 130x faster and can be trained in minutes instead of hours, way faster than competitors.
