Cyrus Vachha

Cyrus Vachha

We present a pipeline for integrating NeRFs into traditional compositing VFX pipelines using Nerfstudio, an open-source framework for training and rendering NeRFs. Our approach involves using Blender, a widely used open-source 3D creation software, to align camera paths and composite NeRF renders with meshes and other NeRFs, allowing for seamless integration of NeRFs into traditional VFX pipelines. Our NeRF Blender add-on allows for more controlled camera trajectories of photorealistic scenes, compositing meshes and other environmental effects with NeRFs, and compositing multiple NeRFs in a single scene.This approach of generating NeRF aligned camera paths can be adapted to other 3D tool sets and workflows, enabling a more seamless integration of NeRFs into visual effects and film production. Documentation can be found here: https://docs.nerf.studio/extensions/blender_addon.html

Cyrus Vachha, Yixiao Kang, Zach Dive et al.

Authoring 3D scenes is a central task for spatial computing applications. Competing visions for lowering existing barriers are (1) focus on immersive, direct manipulation of 3D content or (2) leverage AI techniques that capture real scenes (3D Radiance Fields such as, NeRFs, 3D Gaussian Splatting) and modify them at a higher level of abstraction, at the cost of high latency. We unify the complementary strengths of these approaches and investigate how to integrate generative AI advances into real-time, immersive 3D Radiance Field editing. We introduce Dreamcrafter, a VR-based 3D scene editing system that: (1) provides a modular architecture to integrate generative AI algorithms; (2) combines different levels of control for creating objects, including natural language and direct manipulation; and (3) introduces proxy representations that support interaction during high-latency operations. We contribute empirical findings on control preferences and discuss how generative AI interfaces beyond text input enhance creativity in scene editing and world building.

Yuanbo Chen, Yixiao Kang, Yukun Song et al.

In this system, we discuss methods to stylize a scene of 3D primitive objects into a higher fidelity 3D scene using novel 3D representations like NeRFs and 3D Gaussian Splatting. Our approach leverages existing image stylization systems and image-to-3D generative models to create a pipeline that iteratively stylizes and composites 3D objects into scenes. We show our results on adding generated objects into a scene and discuss limitations.