Creating 3D assets from single-view images is a complex task that demands a
deep understanding of the world. Recently, feed-forward 3D generative models
have made significant progress by training large reconstruction models on
extensive 3D datasets, with triplanes being the preferred 3D geometry
representation. However, effectively utilizing the geometric priors of
triplanes, while minimizing artifacts caused by generated inconsistent
multi-view images, remains a challenge. In this work, we present
\textbf{Fre}quency modulat\textbf{e}d tri\textbf{plane} (\textbf{Freeplane}), a
simple yet effective method to improve the generation quality of feed-forward
models without additional training. We first analyze the role of triplanes in
feed-forward methods and find that the inconsistent multi-view images introduce
high-frequency artifacts on triplanes, leading to low-quality 3D meshes. Based
on this observation, we propose strategically filtering triplane features and
combining triplanes before and after filtering to produce high-quality textured
meshes. These techniques incur no additional cost and can be seamlessly
integrated into pre-trained feed-forward models to enhance their robustness
against the inconsistency of generated multi-view images. Both qualitative and
quantitative results demonstrate that our method improves the performance of
feed-forward models by simply modulating triplanes. All you need is to modulate
the triplanes during inference.

通过对三角面的调制，我们提出了一种简单而有效的方法，用于提高前馈模型的生成质量，无需额外训练，且能够无缝集成到预训练模型中以增强其对生成多视图图像不一致性的鲁棒性。

Freeplane: 三面战机稀疏视图重建模型中的自由午餐解锁

Freeplane: Unlocking Free Lunch in Triplane-Based Sparse-View  Reconstruction Models

With the development of feed-forward models, the default model for sequence
modeling has gradually evolved to replace recurrent networks. Many powerful
feed-forward models based on convolutional networks and attention mechanism
were proposed and show more potential to handle sequence modeling tasks. We
wonder that is there an architecture that can not only achieve an approximate
substitution of recurrent network, but also absorb the advantages of
feed-forward models. So we propose an exploratory architecture referred to
Temporal Convolutional Attention-based Network (TCAN) which combines temporal
convolutional network and attention mechanism. TCAN includes two parts, one is
Temporal Attention (TA) which captures relevant features inside the sequence,
the other is Enhanced Residual (ER) which extracts shallow layer's important
information and transfers to deep layers. We improve the state-of-the-art
results of bpc/perplexity to 26.92 on word-level PTB, 1.043 on character-level
PTB, and 6.66 on WikiText-2.

我们提出了一种基于时间卷积网络和注意力机制的探索性架构称为 TCAN，它不仅能够实现递归网络的近似替代，还可以吸收前向模型的优势，提高了 word-level PTB、character-level PTB 和 WikiText-2 等文本数据集的 bpc/perplexity 表现.