diff --git a/README.md b/README.md
index a09ce055dfa4464523deae2f73228e41203162d3..4cf4c10c84632d40d71a8ff7ca8b1e3d0e28b756 100644
--- a/README.md
+++ b/README.md
@@ -73,7 +73,7 @@ If the build succeeds, you can now run the code via the `build/testbed` executab
 If automatic GPU architecture detection fails, (as can happen if you have multiple GPUs installed), set the `TCNN_CUDA_ARCHITECTURES` enivonment variable for the GPU you would like to use. The following table lists the values for common GPUs. If your GPU is not listed, consult [this exhaustive list](https://developer.nvidia.com/cuda-gpus).
 
 | RTX 30X0 | A100 | RTX 20X0 | TITAN V / V100 | GTX 10X0 / TITAN Xp | GTX 9X0 | K80 |
-|----------|------|----------|----------------|---------------------|---------|-----|
+|:--------:|:----:|:--------:|:--------------:|:-------------------:|:-------:|:---:|
 |       86 |   80 |       75 |             70 |                  61 |      52 |  37 |
 
 
@@ -90,7 +90,7 @@ This codebase comes with an interactive testbed that includes many features beyo
 - And many more task-specific settings.
 - See also our [one minute demonstration video of the tool](https://nvlabs.github.io/instant-ngp/assets/mueller2022instant.mp4).
 
-
+Let's start using the testbed; more information about the GUI and other scripts follow these test scenes.
 
 ### NeRF fox
 
@@ -150,11 +150,48 @@ instant-ngp$ ./build/testbed --mode volume --scene data/volume/wdas_cloud_quarte
 <img src="docs/assets_readme/cloud.png"/>
 
 
+### Testbed controls
+
+Here are the main keyboard controls for the testbed application.
+
+| Key             | Meaning       |
+| :-------------: | ------------- |
+| WASD            | Forward / pan left / backward / pan right. |
+| Spacebar / C    | Move up / down. |
+| = or + / - or _ | Increase / decrease camera velocity. |
+| E / Shift+E     | Increase / decrease exposure. |
+| T               | Toggle training. After around two minutes training tends to settle down, so can be toggled off. |
+| R               | Reload network from file. |
+| Shift+R         | Reset camera. |
+| O               | Toggle visualization or accumulated error map. |
+| G               | Toggle visualization of the ground truth. |
+| M               | Toggle multi-view visualization of layers of the neural model. See the paper's video for a little more explanation. |
+| , / .           | Shows the previous / next visualized layer; hit M to escape. |
+| 1-8             | Switches among various render modes, with 2 being the standard one. You can see the list of render mode names in the control interface. |
+
+There are many controls in the __instant-ngp__ GUI when the testbed program is run.
+First, note that this GUI can be moved and resized, as can the "Camera path" GUI (which first must be expanded to be used).
+
+Some popular user controls in __instant-ngp__ are:
+
+* __Snapshot:__ use Save to save the NeRF solution generated, Load to reload. Necessary if you want to make an animation.
+* __Rendering -> DLSS:__ toggling this on and setting "DLSS sharpening" below it to 1.0 can often improve rendering quality.
+* __Rendering -> Crop size:__ trim back the surrounding environment to focus on the model. "Crop aabb" lets you move the center of the volume of interest and fine tune. See more about this feature in [our NeRF training & dataset tips](https://github.com/NVlabs/instant-ngp/blob/master/docs/nerf_dataset_tips.md).
+
+The "Camera path" GUI lets you set frames along a path. "Add from cam" is the main button you'll want to push, then saving out the camera keyframes using "Save" to create a `base_cam.json` file. There is a bit more information about the GUI [in this post](https://developer.nvidia.com/blog/getting-started-with-nvidia-instant-nerfs/) and [in this (bit dated) video](https://www.youtube.com/watch?v=z3-fjYzd0BA).
+
+
 ## Python bindings
 
 To conduct controlled experiments in an automated fashion, all features from the interactive testbed (and more!) have Python bindings that can be easily instrumented.
 For an example of how the `./build/testbed` application can be implemented and extended from within Python, see `./scripts/run.py`, which supports a superset of the command line arguments that `./build/testbed` does.
 
+Here is a typical command line using `scripts/run.py` to generate a 5-second flythrough of the fox dataset to the (default) file `movie.mp4`, after using the testbed to save a NeRF solution file and a set of camera key frames:
+
+```sh
+instant-ngp$ python scripts/run.py --mode nerf --scene data/nerf/fox --load_snapshot data/nerf/fox/base.msgpack --video_camera_path data/nerf/fox/base_cam.json --video_n_seconds 5 --video_fps 60 --width 1920 --height 1080
+```
+
 If you'd rather build new models from the hash encoding and fast neural networks, consider the [__tiny-cuda-nn__'s PyTorch extension](https://github.com/nvlabs/tiny-cuda-nn#pytorch-extension).
 
 Happy hacking!
diff --git a/scripts/run.py b/scripts/run.py
index d4e4383a4c95d567b223e08f350bdf5920a81810..cd7cdd9e245818270862ee38bd5d76bd0ff6e7a0 100644
--- a/scripts/run.py
+++ b/scripts/run.py
@@ -44,7 +44,7 @@ def parse_args():
 	parser.add_argument("--screenshot_dir", default="", help="Which directory to output screenshots to.")
 	parser.add_argument("--screenshot_spp", type=int, default=16, help="Number of samples per pixel in screenshots.")
 
-	parser.add_argument("--video_camera_path", default="", help="The camera path to render.")
+	parser.add_argument("--video_camera_path", default="", help="The camera path to render, e.g., base_cam.json.")
 	parser.add_argument("--video_camera_smoothing", action="store_true", help="Applies additional smoothing to the camera trajectory with the caveat that the endpoint of the camera path may not be reached.")
 	parser.add_argument("--video_fps", type=int, default=60, help="Number of frames per second.")
 	parser.add_argument("--video_n_seconds", type=int, default=1, help="Number of seconds the rendered video should be long.")
@@ -62,7 +62,7 @@ def parse_args():
 	parser.add_argument("--n_steps", type=int, default=-1, help="Number of steps to train for before quitting.")
 	parser.add_argument("--second_window", action="store_true", help="Open a second window containing a copy of the main output.")
 
-	parser.add_argument("--sharpen", default=0, help="Set amount of sharpening applied to NeRF training images.")
+	parser.add_argument("--sharpen", default=0, help="Set amount of sharpening applied to NeRF training images. Range 0.0 to 1.0.")
 
 
 	args = parser.parse_args()