Tag: differential-rendering

• HeadNeRF A Real-time NeRF-based Parametric Head Model (27 Sep 2022)
  

  HeadNeRF: A Real-time NeRF-based Parametric Head Model provides a parametric head model which could generates photorealistic face images conditioned on identity, expression, head pose and appearance (lighting). Compared with traditional mesh and texture, it provides higher fidelity, inherently differetiable and doesn’t required a 3D dataset; compared with GAN, it provides rendering at different head pose with accurate 3D information. This is achived with NeRF. In addition, it could render in real time (5ms) with a model GPU.

• pixelNeRF Neural Radiance Fields from One or Few Images (26 Sep 2022)
  

  pixelNeRF: Neural Radiance Fields from One or Few Images tries to learn a discontinuous neutral scene representation from one or few input images. To this end, pixelNeRF introduced an architecture that conditions a NeRF on image inputs in a fully convolutional manner. This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one).

• NeuMan Neural Human Radiance Field from a Single Video (26 Sep 2022)
  

  NeuMan: Neural Human Radiance Field from a Single Video proposes a novel framework to reconstruct the human and the scene that can be ren- dered with novel human poses and views from just a single in-the-wild video. Given a video captured by a moving camera, we train two NeRF models: a human NeRF model (condition on SMPL) and a scene NeRF model. Our method is able to learn subject specific details, including cloth wrinkles and ac- cessories, from just a 10 seconds video clip, and to provide high quality renderings of the human under novel poses, from novel views, together with the background.

• Nerfies Deformable Neural Radiance Fields (26 Sep 2022)
  

  Nerfies: Deformable Neural Radiance Fields present the first method capable ofphotorealistically reconstructing deformable scenes using photos/videos cap- tured casually from mobile phones. Our approach augments neural radiance fields (NeRF) by optimizing an additional continuous volumetric deformation field that warps each observed point into a canonical 5D NeRF. To avoid local minima, we propose a coarse-to-fine optimization method for coordinate-based models that allows for more robust optimization. To avoid overfit, we propose an elastic regularization ofthe deformation field that further improves robustness.

• NeRF in the Wild (25 Sep 2022)
  

  This note discusses NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. NeRF-W addresses the central limitation of NeRF that we address here is its assumption that the world is geometrically, materially, and photometrically static — that the density and radiance of the world is constant. NeRF-W instead models per-image appearance variations (such as exposure, lighting, weather) as well as model the scene as the union of shared and image-dependent elements, thereby enabling the unsuper- vised decomposition of scene content into “static” and “transient” components.

• GIRAFFE Representing Scenes as Compositional Generative Neural Feature Fields (25 Sep 2022)
  

  This is my reading note for GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields. The paper aims to provide more control to 3D object rendering NeRF. For example moving the objects in the 3D scene, adding/deleting objects and so on. To acheive this, GIRAFFE proposed to model the objects and background in the scene separately and then composite together for the rendering. In addition, different from NeRF, GIRAFFE uses a learned discriminator instead of L2 or L1 loss as loss function, thus it is a GAN.

• Encoding Method for NERF (24 Sep 2022)
  

  Instant Neural Graphics Primitives with a Multiresolution Hash Encoding tries to reduce inference cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality. This is achieved via a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are op- timized through stochastic gradient descent.

• Neural Radiance Field (15 Apr 2022)
  

  Neural Radiance Field (NeRF), you may have heard words many times for the past few months. Yes, this is the latest progress of neutral work and computer graphics. NeRF represents a scene with learned, continuous volumetric radiance field \(F_{\theta}\) defined over a bounded 3D volume. In Nerf, \(F_{\theta}\) is a multilayer perceptron (MLP) that takes as input a 3D position \(x=(x,y,z)\) and unit-norm viewing direction \(d=(d_x,d_y,d_z)\), and produces as output a density \(\sigma\) and color \(c=(r,g,b)\). By enumerating all most position and direction for a bounded 3D volumne, we could obtain the 3D scene.