We present Splat-SAP, a feed-forward approach to render novel views of human-centered scenes from binocular cameras with large sparsity. Gaussian Splatting has shown its promising potential in rendering tasks, but it typically necessitates per-scene optimization with dense input views. Although some recent approaches achieve feed-forward Gaussian Splatting rendering through geometry priors obtained by multi-view stereo, such approaches still require largely overlapped input views to establish the geometry prior. To bridge this gap, we leverage pixel-wise point map reconstruction to represent geometry which is robust to large sparsity for its independent view modeling. In general, we propose a two-stage learning strategy. In stage 1, we transform the point map into real space via an iterative affinity learning process, which facilitates camera control in the following. In stage 2, we project point maps of two input views onto the target view plane and refine such geometry via stereo matching. Furthermore, we anchor Gaussian primitives on this refined plane in order to render high-quality images. As a metric representation, the scale-aware point map in stage 1 is trained in a self-supervised manner without 3D supervision and stage 2 is supervised with photo-metric loss. We collect multi-view human-centered data and demonstrate that our method improves both the stability of point map reconstruction and the visual quality of free-viewpoint rendering.

Recent 4D Gaussian Splatting (4DGS) methods for reconstructing dynamic scenes have achieved unified spatiotemporal modeling under densely captured multi-view inputs. Nevertheless, reconstruction from sparse-view video sequences remains highly challenging due to unreliable Gaussian initialization and insufficient photometric supervision. We present STAR-GS, a novel framework for sparse-view 4D reconstruction that integrates Spatio-Temporal Geometry Alignment and Generative View Refinement. To address unreliable Gaussian initialization, we develop the Geometry Alignment that combines feed-forward geometry prediction with cross-temporal joint camera optimization, resolving inter-frame similarity ambiguities and establishing a unified global Gaussian initialization. To compensate for insufficient supervision, we further incorporate the Generative View Refinement based on a reference-conditioned single-step diffusion model, which synthesizes high-fidelity novel views to provide dense and temporally consistent photometric guidance for 4DGS optimization. Extensive experiments demonstrate that STAR-GS significantly improves reconstruction quality under sparse-view settings. Ablation studies further validate the effectiveness and complementary contributions of the proposed components.

We present EvHDR-NeRF to recover a High Dynamic Range (HDR) radiance field from event streams and a set of Low Dynamic Range (LDR) views with single exposures. Using the EvHDR-NeRF, we can generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the new relationship between events streams and LDR images, which considers both the Camera Response Function (CRF) and exposure time. Based on this relationship, we categorize events into inter-frame events and intra-exposure. The former is utilized for building HDR radiance field and the latter is used to deblur potentially blurred images. Compared to existing methods, this method can effectively reconstruct the HDR radiance field even when the input images are degraded. Experimental results demonstrate that our method achieves state-of-the-art HDR reconstruction, providing a more adaptable and accurate solution for complex imaging applications.

We present an efficient approach for generalizable clothed human digitalization, termed GBC-Splat. Unlike previous methods that necessitate per-subject optimizations or discount watertight geometry, the proposed method is dedicated to reconstructing complete human shapes and Gaussian Splatting via sparse view RGB inputs in a feed-forward manner. We first extract a fine-grained mesh using a combination of implicit occupancy field regression and explicit disparity estimation between views. The reconstructed high-quality geometry allows us to easily anchor Gaussian primitives to mesh surface according to surface normal and texture, which allows 6-DoF photorealistic novel view synthesis. In addition, we introduce a simple yet effective algorithm to subdivide Gaussian primitives in high-frequency areas to further enhance the visual quality. Without the assistance of human parametric models, our method can tackle loose garments, such as dresses and costumes. Our method outperforms state-of-the-art methods in terms of novel view synthesis while keeping high efficiency, enabling the potential of deployment in real-time applications.

A crucial reason for the success of existing NeRF-based methods is to build a neural density field for the geometry representation via multiple perceptron layers (MLPs). MLPs are continuous functions, however, real geometry or density field is frequently discontinuous at the interface between the air and the surface. Such a contrary brings the problem of unfaithful geometry representation. To this end, this paper proposes spiking NeRF, which leverages spiking neurons and a hybrid Artificial Neural Network (ANN)-Spiking Neural Network (SNN) framework to build a discontinuous density field for faithful geometry representation. Specifically, we first demonstrate the reason why continuous density fields will bring inaccuracy. Then, we propose to use the spiking neurons to build a discontinuous density field. We conduct a comprehensive analysis for the problem of existing spiking neuron models and then provide the numerical relationship between the parameter of the spiking neuron and the theoretical accuracy of geometry. Based on this, we propose a bounded spiking neuron to build the discontinuous density field. Our method achieves SOTA performance. The source code and the supplementary material are available at https://github.com/liaozhanfeng/Spiking-NeRF.