3.2 KiB
3.2 KiB
Abstract Template B Examples (Challenge -> Insight -> Contribution)
\section{Abstract}
% Task
%% Example 1: In recent years, generative models have undergone significant advancement due to the success of diffusion models.
%% Example 2: This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views.
% Technical challenge for previous methods (discuss around the technical challenge that we solved)
%% Example 1: The success of these models is often attributed to their use of guidance techniques, such as classifier and classifier-free methods, which provides effective mechanisms to tradeoff between fidelity and diversity. However, these methods are not capable of guiding a generated image to be aware of its geometric configuration, e.g., depth, which hinders the application of diffusion models to areas that require a certain level of depth awareness.
%% Example 2: Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse.
% Introduce the insight for solving the challenge in one sentence
%% Example 1: To address this limitation, we propose a novel guidance approach for diffusion models that uses estimated depth information derived from the rich intermediate representations of diffusion models.
%% Example 2: To solve this ill-posed problem, our key idea is to integrate observations over video frames.
% Introduce the technical contribution that implements the insight in one to two sentences (usually mention the technical term/name only, without describing every detailed step. The term should be easy to understand and should not create a jump in reading. This ability is very important for writing a good abstract.)
%% Example 1: To do this, we first present a label-efficient depth estimation framework using the internal representations of diffusion models. At the sampling phase, we utilize two guidance techniques to self-condition the generated image using the estimated depth map, the first of which uses pseudo-labeling, and the subsequent one uses a depth-domain diffusion prior.
%% Example 2: To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh
% Introduce the benefits of technical novelty
%% Example 2: so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently.
% Experiment
Given example pattern 2
This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views.... representation learning will be ill-posed if the views are highly sparse.To solve this ill-posed problem, our key idea is to integrate observations over video frames.To this end, we propose Neural Body ...... observations across frames can be naturally integrated ... provides geometric guidance ...Experiments show [main result].