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This comprehensive review examines the rapid advances in generative AI for multimodal image synthesis and editing, covering visual, textual, and audio guidance, model families such as GANs, diffusion, autoregressive, and NeRF, as well as datasets, challenges, and future research directions.
Professor Hu Qiang presented recent progress in Neural Radiance Fields—covering implicit/explicit representations, hybrid models, and solutions for dynamic scenes, cloud‑based and edge‑cloud rendering—while also reviewing generative AI advances such as diffusion‑based text‑to‑image/video/3D, LoRA fine‑tuning, and large‑scale story‑book datasets, highlighting applications in virtual‑real content, smart‑city modeling, and 6‑DoF e‑commerce displays.
Alibaba’s Taobao engineers detail how they transformed slow, high‑quality NeRF reconstruction into a real‑time mobile solution by combining an Octree‑Tiny‑MLP architecture, SNeRG optimizations, and a high‑frequency voxel reduction that shrank models to ~5 MB and achieved ~6 FPS on low‑end Android phones, targeting sub‑1 MB models and 50 FPS.
The article presents OPPO's latest research on virtual human audio‑lip and RGB driving, multimodal learning breakthroughs such as CETNETs and cross‑modal matching, and a reflective discussion on the challenges and future directions of general artificial intelligence, highlighting the interconnections among these three domains.
Alibaba’s DaTaobao team presented six CVPR 2022 papers—covering video restoration, real‑time image enhancement, human‑object interaction detection, indirect illumination modeling, editable NeRFs, and robust NeRF reconstruction—with all code and models open‑sourced, and invites viewers to a live stream for detailed walkthroughs.
RapNeRF enhances Neural Radiance Fields for extreme view extrapolation by introducing Random Ray Casting and a Ray Atlas, which together augment training data and store view‑dependent surface features, enabling robust, high‑quality novel‑view synthesis from sparse images and outperforming prior methods on synthetic and real datasets.
NeRF‑Editing introduces an interactive framework that lets users freely deform the geometry of neural radiance fields by coupling an explicit mesh with implicit NeRF representations, propagating mesh vertex changes through tetrahedral ARAP optimization to bend rays during rendering, enabling realistic edits and animations on synthetic and real‑world scenes, a first reported at CVPR 2022.