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+++ b/docs/nerfology/methods/tensorf.md
@@ -8,6 +8,10 @@
 Paper Website
 ```
 
+<video id="teaser" muted autoplay playsinline loop controls width="100%">
+    <source id="mp4" src="https://apchenstu.github.io/TensoRF/video/train_process.mp4" type="video/mp4">
+</video>
+
 ### Running Model
 
 ```bash
@@ -16,4 +20,14 @@ ns-train tensorf
 
 ## Overview
 
-🚧 Guide under construction 🚧
+```{image} imgs/tensorf/models_tensorf_pipeline.png
+:align: center
+```
+
+TensoRF models the radiance field of a scene as a 4D tensor, which represents a 3D voxel grid with per-voxel multi-channel features. TensoRF factorizes the 4D scene tensor into multiple compact low-rank tensor components using CP or VM modes. CP decomposition factorizes tensors into rank-one components with compact vectors. Vector-Matrix (VM) decomposition factorizes tensors into compact vector and matrix factors.
+
+```{image} imgs/tensorf/models_tensorf_factorization.png
+:align: center
+```
+
+TensoRF with CP(left) and VM(right) decompositions results in a significantly reduced memory footprint compared to previous and concurrent works that directly optimize per-voxel features, such as [Plenoxels](https://alexyu.net/plenoxels/) and [PlenOctrees](https://alexyu.net/plenoctrees/). In experiments, TensoRF with CP decomposition achieves fast reconstruction with improved rendering quality and a smaller model size compared to NeRF. Furthermore, TensoRF with VM decomposition enhances rendering quality even further, while reducing reconstruction time and maintaining a compact model size.