diff --git a/talkinghead/tha3/app/util.py b/talkinghead/tha3/app/util.py
index d14dd02..f8697bf 100644
--- a/talkinghead/tha3/app/util.py
+++ b/talkinghead/tha3/app/util.py
@@ -5,7 +5,8 @@ __all__ = ["posedict_keys", "posedict_key_to_index",
            "posedict_to_pose", "pose_to_posedict",
            "maybe_install_models",
            "torch_image_to_numpy", "to_talkinghead_image",
-           "RunningAverage"]
+           "RunningAverage",
+           "rgb_to_yuv", "yuv_to_rgb", "luminance"]
 
 import logging
 import json
@@ -199,6 +200,102 @@ def to_talkinghead_image(image: PIL.Image, new_size: Tuple[int] = (512, 512)) ->
                             (new_size[1] - image.size[1]) // 2))
     return new_image
 
+# --------------------------------------------------------------------------------
+# Colorspace conversion
+#
+# Some postprocessor filters need a colorspace that separates the brightness information from the colors.
+# For simplicity, and considering that the filters are meant to simulate TV technology, we use YUV.
+# (Also, it's familiar to those who have worked on the technical side of digital video.)
+#
+# RGB<->YCbCr conversion based on:
+#   https://github.com/TheZino/pytorch-color-conversions/
+#   https://gist.github.com/yohhoy/dafa5a47dade85d8b40625261af3776a
+#   https://www.itu.int/rec/R-REC-BT.601 (SDTV)
+#   https://www.itu.int/rec/R-REC-BT.709 (HDTV)
+#   https://www.itu.int/rec/R-REC-BT.2020 (UHDTV)
+#   https://en.wikipedia.org/wiki/Relative_luminance
+#   https://en.wikipedia.org/wiki/Luma_(video)
+#   https://en.wikipedia.org/wiki/Chrominance
+#   https://en.wikipedia.org/wiki/YUV
+
+# # Original approach of https://github.com/TheZino/pytorch-color-conversions/
+# _RGB_TO_YCBCR = torch.tensor([[0.257, 0.504, 0.098],
+#                               [-0.148, -0.291, 0.439],
+#                               [0.439, -0.368, -0.071]])  # BT.601 (SDTV)
+# _YCBCR_TO_RGB = torch.inverse(_RGB_TO_YCBCR)
+# _YCBCR_OFF = torch.tensor([0.063, 0.502, 0.502])  # zero point: limited range black Y = 16/255, zero chroma U = V = 128/255
+# def _colorspace_convert_mul(coeffs: torch.tensor, image: torch.tensor) -> torch.tensor:
+#     return torch.einsum("rc,cij->rij", (coeffs.to(image.dtype).to(image.device), image))
+#
+# def _rgb_to_yuv(image: torch.tensor) -> torch.tensor:
+#     YUV_zero = _YCBCR_OFF.to(image.dtype).to(image.device)
+#     image_yuv = _colorspace_convert_mul(_RGB_TO_YCBCR, image) + YUV_zero
+#     return torch.clamp(image_yuv, 0.0, 1.0)
+#
+# def _yuv_to_rgb(image: torch.tensor) -> torch.tensor:
+#     YUV_zero = _YCBCR_OFF.to(image.dtype).to(image.device)
+#     image_rgb = _colorspace_convert_mul(_YCBCR_TO_RGB, image - YUV_zero)
+#     return torch.clamp(image_rgb, 0.0, 1.0)
+
+# Note that since we are working in *linear* (i.e. before gamma) RGB space,
+# Y is the true relative luminance (it is NOT the luma Y').
+#
+# Y  = a * R + b * G + c * B
+# Cb = (B - Y) / d
+# Cr = (R - Y) / e
+#
+# Here the chroma coordinates Cb and Cr are in [-0.5, 0.5].
+# The inverse conversion is:
+#
+# R = Y + e * Cr
+# G = Y - (a * e / b) * Cr - (c * d / b) * Cb
+# B = Y + d * Cb
+#
+# where
+#
+#    BT.601  BT.709  BT.2020
+# a  0.299   0.2126  0.2627
+# b  0.587   0.7152  0.6780
+# c  0.114   0.0722  0.0593
+# d  1.772   1.8556  1.8814
+# e  1.402   1.5748  1.4746
+#
+# Let's fully solve the first system for YCbCr in terms of RGB:
+#
+# Y  = a * R + b * G + c * B
+# Cb = (B - (a * R + b * G + c * B)) / d
+# Cr = (R - (a * R + b * G + c * B)) / e
+#
+# Y  = a * R + b * G + c * B
+# Cb = (- a * R - b * G + (1 - c) * B) / d
+# Cr = ((1 - a) * R - b * G - c * B) / e
+#
+# so YCbCr = M * RGB, where the matrix M is:
+#
+a, b, c, d, e = [0.2126, 0.7152, 0.0722, 1.8556, 1.5748]  # ITU-R Rec. 709 (HDTV)
+_RGB_TO_YCBCR = torch.tensor([[a, b, c],
+                              [-a / d, -b / d, (1.0 - c) / d],
+                              [(1.0 - a) / e, -b / e, -c / e]])
+_YCBCR_TO_RGB = torch.inverse(_RGB_TO_YCBCR)
+def _colorspace_convert_mul(coeffs: torch.tensor, image: torch.tensor) -> torch.tensor:
+    return torch.einsum("rc,cij->rij", (coeffs.to(image.dtype).to(image.device), image))
+
+def rgb_to_yuv(image: torch.tensor) -> torch.tensor:
+    """RGB (linear) 0...1 -> YUV, where Y = 0...1, U, V = -0.5 ... +0.5"""
+    return _colorspace_convert_mul(_RGB_TO_YCBCR, image)
+
+def yuv_to_rgb(image: torch.tensor, clamp: bool = True) -> torch.tensor:
+    """Inverse of `rgb_to_yuv`, which see, optionally clamping the resulting RGB image to [0, 1]."""
+    image_rgb = _colorspace_convert_mul(_YCBCR_TO_RGB, image)
+    if clamp:
+        image_rgb = torch.clamp(image_rgb, 0.0, 1.0)
+    return image_rgb
+
+_RGB_TO_Y = _RGB_TO_YCBCR[0, :]
+def luminance(image: torch.tensor) -> torch.tensor:
+    """RGB (linear 0...1) -> Y (true relative luminance)"""
+    return torch.einsum("c,cij->ij", (_RGB_TO_Y.to(image.dtype).to(image.device), image))
+
 # --------------------------------------------------------------------------------
 
 class RunningAverage: